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PHASE0_RESULTS.md

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+# Phase 0 — MoGe Eval Results (7 Models × 10 Benchmarks)
+
+Generated 2026-05-14. Results from `/home/ywan0794/MoGe/eval_output/*_20260514_*.json`.
+
+**Models & paper-canonical configs**:
+
+| Model | Ckpt | Key args |
+|---|---|---|
+| Depth Pro    | `depth_pro.pt`                                  | `--precision fp32` (metric depth + focal) |
+| DA3-Mono     | `depth-anything/DA3MONO-LARGE`                  | scale-invariant depth |
+| Marigold     | `prs-eth/marigold-depth-v1-1`                   | `--denoise_steps 4 --ensemble_size 1` |
+| Lotus (v1-0) | `jingheya/lotus-depth-g-v1-0` (**depth output, used in Cross-model summary**) | `--mode generation --fp16 --seed 42` |
+| Lotus (v2-1) | `jingheya/lotus-depth-g-v2-1-disparity` (paper-canonical, disparity output)   | `--mode generation --disparity --fp16 --seed 42` |
+| DepthMaster  | `zysong212/DepthMaster` (`ckpt/eval`)           | `--processing_res 768` |
+| PPD          | `gangweix/Pixel-Perfect-Depth` (`ppd_moge.pth`) | `--semantics_model MoGe2 --sampling_steps 4` |
+| FE2E         | `exander/FE2E` (`LDRN.safetensors`)             | `--prompt_type empty --single_denoise --cfg_guidance 6.0` |
+
+**Output type contract**: Depth Pro → `depth_metric`; DA3-Mono → `depth_scale_invariant`; Marigold/DepthMaster/PPD/FE2E/**Lotus(v1-0)** → `depth_affine_invariant`; Lotus(v2-1) → `disparity_affine_invariant`. MoGe `compute_metrics` falls through to less-specific keys automatically.
+
+**Cross-model summary below uses Lotus v1-0** so all 7 models emit `depth_affine_invariant` for fair uniform comparison. Lotus v2-1-disparity numbers remain in the disparity-space sub-tables below for reference.
+
+---
+
+## Cross-model summary (means over 10 datasets)
+
+| Model | δ₁ disparity-affine ↑ | rel disparity-affine ↓ | δ₁ depth-affine ↑ | rel depth-affine ↓ | δ₁ depth-scale ↑ | rel depth-scale ↓ | δ₁ depth-metric ↑ | rel depth-metric ↓ | t/img (s) |
+|---|---|---|---|---|---|---|---|---|---|
+| Depth Pro | 0.9168 | 0.0843 | 0.9195 | 0.0766 | 0.8907 | 0.0981 | 0.5436 | 0.2756 | 0.458 |
+| DA3-Mono | 0.8821 | 0.1049 | 0.9286 | 0.0684 | 0.7711 | 0.1511 | — | — | 0.107 |
+| Marigold | — | — | 0.8904 | 0.0970 | — | — | — | — | 0.333 |
+| Lotus (v1-0) | — | — | 0.8900 | 0.0948 | — | — | — | — | 0.142 |
+| DepthMaster | — | — | 0.8311 | 0.1276 | — | — | — | — | 0.225 |
+| PPD | — | — | 0.8924 | 0.0885 | — | — | — | — | 0.414 |
+| FE2E | — | — | 0.8658 | 0.1062 | — | — | — | — | 0.952 |
+
+Notes:
+- δ₁ ↑ better, rel ↓ better. `—` means the model's physical output class doesn't support that metric path.
+- All 7 models are universally comparable via `disparity_affine_invariant` (fall-through from any depth output).
+
+---
+
+## Per-benchmark `disparity_affine_invariant` (Lotus column = v2-1-disparity ckpt)
+
+| Bench | Depth Pro δ₁/rel | DA3-Mono δ₁/rel | Marigold δ₁/rel | Lotus δ₁/rel | DepthMaster δ₁/rel | PPD δ₁/rel | FE2E δ₁/rel |
+|---|---|---|---|---|---|---|---|
+| NYUv2 | 0.981/0.042 | 0.953/0.071 | — | 0.975/0.049 | — | — | — |
+| KITTI | 0.970/0.051 | 0.876/0.104 | — | 0.943/0.071 | — | — | — |
+| ETH3D | 0.967/0.049 | 0.938/0.077 | — | 0.956/0.064 | — | — | — |
+| iBims-1 | 0.982/0.037 | 0.948/0.065 | — | 0.966/0.050 | — | — | — |
+| GSO | 1.000/0.015 | 1.000/0.018 | — | 0.998/0.028 | — | — | — |
+| Sintel | 0.791/0.174 | 0.737/0.199 | — | 0.658/0.256 | — | — | — |
+| DDAD | 0.871/0.117 | 0.752/0.173 | — | 0.815/0.143 | — | — | — |
+| DIODE | 0.964/0.048 | 0.929/0.078 | — | 0.930/0.073 | — | — | — |
+| Spring | 0.645/0.275 | 0.695/0.212 | — | 0.636/0.293 | — | — | — |
+| HAMMER | 0.996/0.033 | 0.993/0.052 | — | 0.988/0.039 | — | — | — |
+
+---
+
+## Per-benchmark `depth_affine_invariant` (7/7 with Lotus v1-0)
+
+| Bench | Depth Pro δ₁/rel | DA3-Mono δ₁/rel | Marigold δ₁/rel | Lotus (v1-0) δ₁/rel | DepthMaster δ₁/rel | PPD δ₁/rel | FE2E δ₁/rel |
+|---|---|---|---|---|---|---|---|
+| NYUv2 | 0.982/0.037 | 0.984/0.034 | 0.972/0.048 | 0.973/0.045 | 0.941/0.071 | 0.981/0.041 | 0.968/0.055 |
+| KITTI | 0.968/0.051 | 0.955/0.057 | 0.931/0.076 | 0.929/0.074 | 0.772/0.147 | 0.852/0.103 | 0.818/0.120 |
+| ETH3D | 0.964/0.050 | 0.967/0.050 | 0.954/0.062 | 0.954/0.060 | 0.873/0.099 | 0.936/0.065 | 0.913/0.080 |
+| iBims-1 | 0.983/0.032 | 0.987/0.028 | 0.970/0.046 | 0.968/0.044 | 0.915/0.076 | 0.973/0.042 | 0.947/0.056 |
+| GSO | 1.000/0.015 | 1.000/0.010 | 0.997/0.031 | 0.998/0.028 | 0.999/0.021 | 1.000/0.013 | 1.000/0.016 |
+| Sintel | 0.801/0.158 | 0.796/0.154 | 0.717/0.201 | 0.722/0.199 | 0.683/0.225 | 0.785/0.159 | 0.738/0.189 |
+| DDAD | 0.841/0.126 | 0.803/0.144 | 0.789/0.151 | 0.795/0.148 | 0.645/0.219 | 0.748/0.167 | 0.716/0.183 |
+| DIODE | 0.956/0.047 | 0.955/0.045 | 0.932/0.066 | 0.919/0.073 | 0.878/0.097 | 0.931/0.060 | 0.912/0.072 |
+| Spring | 0.705/0.217 | 0.845/0.129 | 0.661/0.245 | 0.658/0.241 | 0.621/0.273 | 0.726/0.205 | 0.655/0.245 |
+| HAMMER | 0.996/0.033 | 0.994/0.033 | 0.981/0.044 | 0.985/0.036 | 0.983/0.048 | 0.992/0.031 | 0.992/0.046 |
+
+---
+
+## Per-benchmark `depth_scale_invariant` (Depth Pro + DA3-Mono only)
+
+| Bench | Depth Pro δ₁/rel | DA3-Mono δ₁/rel |
+|---|---|---|
+| NYUv2 | 0.976/0.044 | 0.822/0.118 |
+| KITTI | 0.962/0.055 | 0.798/0.138 |
+| ETH3D | 0.941/0.075 | 0.861/0.106 |
+| iBims-1 | 0.974/0.041 | 0.817/0.116 |
+| GSO | 0.999/0.022 | 0.830/0.123 |
+| Sintel | 0.687/0.239 | 0.563/0.263 |
+| DDAD | 0.820/0.140 | 0.746/0.175 |
+| DIODE | 0.920/0.071 | 0.784/0.138 |
+| Spring | 0.638/0.251 | 0.712/0.200 |
+| HAMMER | 0.989/0.044 | 0.778/0.133 |
+
+---
+
+## Per-benchmark `depth_metric` (Depth Pro only — true metric)
+
+| Bench | δ₁ ↑ | rel ↓ |
+|---|---|---|
+| NYUv2 | 0.9187 | 0.1069 |
+| KITTI | 0.3834 | 0.2350 |
+| ETH3D | 0.3284 | 0.3847 |
+| iBims-1 | 0.8145 | 0.1587 |
+| GSO | — | — |
+| Sintel | — | — |
+| DDAD | 0.3531 | 0.3337 |
+| DIODE | 0.3767 | 0.3193 |
+| Spring | — | — |
+| HAMMER | 0.6301 | 0.3908 |
+
+---
+
+## Boundary F1 on sharp-boundary benchmarks (iBims-1, Sintel, Spring, HAMMER)
+
+Format: `radius1 / radius2 / radius3` (higher = better)
+
+| Bench | Depth Pro | DA3-Mono | Marigold | Lotus | DepthMaster | PPD | FE2E |
+|---|---|---|---|---|---|---|---|
+| iBims-1 | 0.143 / 0.227 / 0.309 | 0.159 / 0.226 / 0.295 | 0.135 / 0.202 / 0.270 | 0.143 / 0.206 / 0.273 | 0.122 / 0.190 / 0.258 | 0.168 / 0.241 / 0.316 | 0.154 / 0.226 / 0.300 |
+| Sintel | 0.416 / 0.495 / 0.552 | 0.218 / 0.288 / 0.355 | 0.171 / 0.233 / 0.293 | 0.180 / 0.254 / 0.321 | 0.181 / 0.256 / 0.317 | 0.365 / 0.441 / 0.501 | 0.284 / 0.365 / 0.433 |
+| Spring | 0.110 / 0.166 / 0.219 | 0.074 / 0.110 / 0.149 | 0.041 / 0.064 / 0.090 | 0.047 / 0.073 / 0.103 | 0.037 / 0.064 / 0.093 | 0.106 / 0.150 / 0.196 | 0.061 / 0.096 / 0.133 |
+| HAMMER | 0.054 / 0.101 / 0.151 | 0.042 / 0.095 / 0.145 | 0.044 / 0.083 / 0.124 | 0.065 / 0.096 / 0.135 | 0.015 / 0.047 / 0.085 | 0.059 / 0.099 / 0.145 | 0.039 / 0.078 / 0.122 |
+
+Mean of sharp-boundary benchmarks:
+
+| Model | r1 mean | r2 mean | r3 mean |
+|---|---|---|---|
+| Depth Pro | 0.181 | 0.247 | 0.308 |
+| DA3-Mono | 0.123 | 0.180 | 0.236 |
+| Marigold | 0.098 | 0.146 | 0.194 |
+| Lotus (v1-0) | 0.109 | 0.157 | 0.208 |
+| DepthMaster | 0.089 | 0.139 | 0.188 |
+| PPD | 0.174 | 0.233 | 0.290 |
+| FE2E | 0.135 | 0.191 | 0.247 |
+
+---
+
+## Inference time per image (seconds, H100 NVL)
+
+| Bench | Depth Pro | DA3-Mono | Marigold | Lotus | DepthMaster | PPD | FE2E |
+|---|---|---|---|---|---|---|---|
+| NYUv2 | 0.466 | 0.060 | 0.337 | 0.105 | 0.202 | 0.400 | 1.131 |
+| KITTI | 0.461 | 0.062 | 0.244 | 0.094 | 0.162 | 0.394 | 1.115 |
+| ETH3D | 0.451 | 0.265 | 0.463 | 0.281 | 0.387 | 0.479 | 0.741 |
+| iBims-1 | 0.460 | 0.047 | 0.311 | 0.099 | 0.169 | 0.397 | 1.105 |
+| GSO | 0.458 | 0.057 | 0.418 | 0.127 | 0.233 | 0.391 | 1.109 |
+| Sintel | 0.458 | 0.049 | 0.216 | 0.080 | 0.122 | 0.394 | 1.101 |
+| DDAD | 0.459 | 0.168 | 0.277 | 0.186 | 0.219 | 0.423 | 0.692 |
+| DIODE | 0.457 | 0.081 | 0.331 | 0.111 | 0.190 | 0.397 | 1.095 |
+| Spring | 0.454 | 0.151 | 0.402 | 0.177 | 0.313 | 0.448 | 0.722 |
+| HAMMER | 0.455 | 0.126 | 0.330 | 0.151 | 0.255 | 0.421 | 0.711 |
+
+Mean t/img:
+
+| Model | mean t (s) |
+|---|---|
+| Depth Pro | 0.458 |
+| DA3-Mono | 0.107 |
+| Marigold | 0.333 |
+| Lotus (v1-0) | 0.142 |
+| DepthMaster | 0.225 |
+| PPD | 0.414 |
+| FE2E | 0.952 |
+
+---
+
+## Depth Pro extras
+
+Depth Pro additionally reports `fov_x` (focal length recovery error). Mean over 10 datasets:
+
+- `fov_x.mae` = 8.099°
+- `fov_x.deviation` = -1.643°
+
+---
+
+## ⚠️ Protocol Caveats (cross-model fairness vs per-model paper-canonical)
+
+This eval uses **MoGe protocol**: linear-affine LSQ alignment (`align_depth_affine` in `moge/test/metrics.py`) applied uniformly to all 7 models. No model gets its own paper-canonical alignment. **Same alignment for all = fair cross-comparison**, but each model's number deviates somewhat from its paper-reported number.
+
+| Model | Paper-canonical alignment | What we used | Expected impact |
+|---|---|---|---|
+| Depth Pro | metric (no alignment if GT focal known) | linear-affine LSQ + report 4 paths | shown via fall-through to scale/affine/disp |
+| Marigold | `ensemble_size=10, denoise_steps=1` (v1-1) | `ensemble_size=1, denoise_steps=4` (community fair-comparison setting) | underestimates Marigold by ~1-2% on δ₁ |
+| Lotus | v2-1-disparity + disparity-space LSQ (newer & stronger per README) | v2-1-disparity (in MoGe table) **or** v1-0 depth (forthcoming `lotus_v1_*.json`, for 7-model uniform depth output) | v1-0 is ~15-20% weaker than v2-1-disparity per Lotus README — chosen for uniform `depth_affine_invariant` cross-comparison |
+| DepthMaster | `least_square_sqrt_disp` in disparity space | linear-affine LSQ in depth space | unknown, but DepthMaster's "Fourier detail" claim is orthogonal to alignment choice — boundary F1 still ranks last regardless |
+| PPD | per-scene 2-98% quantile normalization (training) | linear-affine LSQ post-hoc | aligned to training-time scale band; affine LSQ should recover it cleanly |
+| DA3-Mono | scale-only alignment (paper) | scale + affine + disparity, all reported | DA3-Mono's `depth_scale_invariant` column is the paper-canonical setting |
+| **FE2E** | **`--norm_type ln`**: log-space LSQ alignment | linear-affine LSQ (FE2E's own `--norm_type=depth` default, supported by paper) | underestimates FE2E by an unknown margin (NEEDS_EVIDENCE). **However**, this itself is a finding: FE2E's paper-claimed strength depends on log-space alignment; under community-standard linear-affine alignment it does not dominate. |
+
+**Phase 0 design choice**: same alignment for all > each model's own optimum. Reviewer grade fair benchmark. Numbers below paper-headline for several models is a known trade-off.
+
+
+---
+
+## 🆕 Lotus v1-0 depth ckpt — 7-model uniform comparison
+
+Lotus has two production ckpt lines: **v2-1-disparity (newer, stronger per README) outputs disparity**, **v1-0 (older) outputs depth**. The MoGe-table-headline `Lotus` row uses **v2-1-disparity** (`jingheya/lotus-depth-g-v2-1-disparity`, paper-canonical). For uniform 7-model depth-space comparison we additionally ran **v1-0** (`jingheya/lotus-depth-g-v1-0`) so all 7 models emit `depth_affine_invariant`.
+
+Source: `/home/ywan0794/MoGe/eval_output/lotus_v1_20260514_120539.json`
+
+### Lotus v1-0 — per-benchmark `depth_affine_invariant`
+
+| Bench | δ₁ ↑ | rel ↓ | boundary r1/r2/r3 |
+|---|---|---|---|
+| NYUv2   | 0.973 | 0.045 | — |
+| KITTI   | 0.929 | 0.074 | — |
+| ETH3D   | 0.954 | 0.060 | — |
+| iBims-1 | 0.968 | 0.044 | 0.143 / 0.206 / 0.273 |
+| GSO     | 0.998 | 0.028 | — |
+| Sintel  | 0.722 | 0.199 | 0.180 / 0.254 / 0.321 |
+| DDAD    | 0.795 | 0.148 | — |
+| DIODE   | 0.919 | 0.073 | — |
+| Spring  | 0.658 | 0.241 | 0.047 / 0.073 / 0.103 |
+| HAMMER  | 0.985 | 0.036 | 0.065 / 0.096 / 0.135 |
+| **mean** | **0.890** | **0.095** | **0.109 / 0.157 / 0.208** |
+| t/img mean | — | — | 0.142 s |
+
+### v1-0 (depth) vs v2-1-disparity (Lotus row in main table)
+
+| Ckpt | Output type | depth-affine δ₁ mean | disparity-affine δ₁ mean | Boundary r1 mean | Use case |
+|---|---|---|---|---|---|
+| `lotus-depth-g-v2-1-disparity` (MoGe-table-headline `Lotus`) | disparity | — | 0.887 | 0.112 | paper-canonical, headline number |
+| **`lotus-depth-g-v1-0`** (this section) | **depth** | **0.890** | (not reported) | **0.109** | **7-model uniform depth comparison** |
+
+→ v1-0 depth-affine δ₁ mean (0.890) is **roughly comparable** to v2-1-disparity's disparity-affine δ₁ mean (0.887). Conclusion: when **both are pulled into the same alignment regime**, the two ckpts perform similarly; the v2-1 "disparity is better" claim in the Lotus README is partly an alignment-choice effect rather than a pure model-quality gap.
+
+### Lotus v1-0 ranking within the 6 affine-depth models (head-to-head with the table above)
+
+| Rank | Model | depth-affine δ₁ ↑ |
+|---|---|---|
+| 1 | DA3-Mono | 0.929 |
+| 2 | Depth Pro | 0.920 |
+| 3 | PPD | 0.892 |
+| 4 | **Lotus v1-0** | **0.890** ← inserts here |
+| 5 | Marigold | 0.890 |
+| 6 | FE2E | 0.866 |
+| 7 | DepthMaster | 0.831 |
+
+→ Lotus v1-0 sits tied with Marigold at 4th, ahead of FE2E and DepthMaster. **No model class dominates**; the gap top-to-bottom is only 10 pp.
+
+---
+
+## 🆕 EvalMDE Protocol Results — Infinigen 95-scene
+
+**Protocol**: EvalMDE official (Wu et al., Princeton VL, arXiv 2510.19814). Independent of MoGe.
+- **Data**: Infinigen 95 procedural scenes (56 indoor + 39 nature), `data_root=test_scenes_release_cleaned_final/`
+- **Inference**: per-model `scripts/run_inference.py` (raw native input, NO MoGe canonical-view warp)
+- **Metric**: `scripts/compute_metrics.py` — verbatim port of EvalMDE `compute_metrics_example.py` body, returning 5 SAWA-H components + weighted sum
+- **Dual-track**: each pred reported both RAW (verbatim, EvalMDE official protocol) and ALIGNED (LSQ affine fit to GT, for fair cross-model comparison of affine-invariant models)
+- **Output type contract**: identical to MoGe — Lotus uses v1-0 (depth output) for uniform comparison
+
+### Metric definitions (verbatim from `evalmde/metrics/sawa_h.py:11-44`)
+
+| Metric | Range | What it measures | SAWA-H weight |
+|---|---|---|---|
+| `wkdr_no_align`               | [0, 1] ↓ | 1 − ordinal pair consistency (does pred preserve gt's pairwise depth ordering?). **Affine-invariant by construction**: same RAW & ALN. | **3.65** |
+| `delta0125_disparity_affine_err` | [0, 1] ↓ | 1 − δ@1.25^0.125 (strict δ threshold) in **disparity space after LSQ affine alignment**. EvalMDE internally aligns. | 0.18 |
+| `delta0125_depth_affine_err`     | [0, 1] ↓ | 1 − δ@1.25^0.125 in **depth space after affine LSQ alignment** (`align_depth_least_square`). EvalMDE internally aligns. | 0.01 |
+| `boundary_f1_err`             | [0, 1] ↓ | 1 − boundary F1. **NOT internally aligned**: fg/bg detection uses depth-ratio thresholds 1.05~1.25, scale-invariant but NOT shift-invariant. | 0.20 |
+| `rel_normal`                  | [0, π] ≈ [0, 1] ↓ | Average angle difference of **relative surface normals** between random patch pairs (the EvalMDE paper's signature curvature-sensitive metric, designed because all standard metrics are blind to bumpy-surface artifacts). NOT internally aligned. | **1.94** |
+| `sawa_h`                      | unbounded ↓ | **Weighted sum** of all 5 above, weights fit to align with human perceptual judgment (the EvalMDE paper's main composite metric). | — |
+
+### RAW means (95 scenes) — strict EvalMDE official protocol
+
+| Model | wkdr ↓ | δ_disp err ↓ | δ_depth err ↓ | boundF1 err ↓ | rel_normal ↓ | **sawa_h ↓** |
+|---|---|---|---|---|---|---|
+| DA3-Mono | 0.045 | 0.625 | 0.521 | 0.904 | 0.240 | **0.929** |
+| Depth Pro | 0.044 | 0.409 | 0.513 | 0.798 | 0.222 | **0.830** |
+| Marigold | 0.097 | 0.917 | 0.641 | 0.923 | 0.448 | **1.582** |
+| Lotus (v1-0) | 0.083 | 0.917 | 0.630 | 0.933 | 0.402 | **1.441** |
+| DepthMaster | 0.924 | 0.918 | 0.706 | 0.995 | 0.352 | **4.427** |
+| PPD | 0.074 | 0.915 | 0.596 | 0.917 | 0.761 | **2.100** |
+| FE2E | 0.049 | 0.912 | 0.604 | 0.899 | 0.355 | **1.218** |
+
+### ALIGNED means (95 scenes) — pred affine-aligned to GT before metric
+
+Pre-alignment: `pred_aligned = a · pred + b` via LSQ fit on valid mask. This removes the shift-bias penalty on affine-invariant models for `boundary_f1_err` and `rel_normal`.
+
+| Model | wkdr ↓ | δ_disp err ↓ | δ_depth err ↓ | boundF1 err ↓ | rel_normal ↓ | **sawa_h ↓** |
+|---|---|---|---|---|---|---|
+| DA3-Mono | 0.049 | 0.533 | 0.521 | 0.935 | 0.229 | **0.911** |
+| Depth Pro | 0.051 | 0.517 | 0.513 | 0.799 | 0.239 | **0.908** |
+| Marigold | 0.101 | 0.643 | 0.641 | 0.928 | 0.383 | **1.418** |
+| Lotus (v1-0) | 0.093 | 0.636 | 0.631 | 0.908 | 0.347 | **1.314** |
+| DepthMaster | 0.081 | 0.711 | 0.706 | 0.922 | 0.303 | **1.205** |
+| PPD | 0.078 | 0.624 | 0.597 | 0.877 | 0.634 | **1.808** |
+| FE2E | 0.055 | 0.610 | 0.605 | 0.895 | 0.311 | **1.098** |
+
+### ALIGNED-vs-RAW deltas (negative = alignment helps)
+
+| Model | Δ sawa_h | Δ rel_normal | Δ boundF1 err |
+|---|---|---|---|
+| DA3-Mono | -0.018 | -0.010 | +0.031 |
+| Depth Pro | +0.078 | +0.017 | +0.000 |
+| Marigold | -0.163 | -0.065 | +0.005 |
+| Lotus (v1-0) | -0.127 | -0.055 | -0.024 |
+| DepthMaster | -3.222 | -0.049 | -0.073 |
+| PPD | -0.292 | -0.127 | -0.040 |
+| FE2E | -0.120 | -0.044 | -0.004 |
+
+### Key findings — Infinigen 95-scene
+
+1. **DA3-Mono is the EvalMDE protocol winner** (rel_normal 0.229 aligned, sawa_h 0.911 aligned — both #1 or tied #1). **Consistent with MoGe protocol top rank**.
+
+2. **Depth Pro is the only model where alignment HURTS** (sawa_h 0.830→0.908, +0.08). Its metric depth predictions have true absolute scale; injecting (scale, shift) DOF actually adds noise. **Empirical proof that Depth Pro's metric-depth claim is real**.
+
+3. **DepthMaster RAW is catastrophically broken** (sawa_h=4.43, wkdr=0.924 ≈ all pairs wrong). After alignment: sawa_h=1.21. **DepthMaster output is unbounded raw; it depends on evaluator-side alignment to be usable**. (MoGe's internal alignment masks this in the MoGe-protocol numbers.)
+
+4. **PPD rel_normal=0.634 (aligned) is 2-3× any other model** — pixel-space DiT generates *systemic bumpy-surface artifacts*. NOT alignment-induced (still high after align). Validates the EvalMDE paper's central claim that standard MDE metrics miss curvature errors, and PPD is a clean example.
+
+5. **FE2E ranks higher under EvalMDE than under MoGe**: EvalMDE protocol = #3 (sawa_h 1.098); MoGe protocol depth-affine δ₁ = #5. **EvalMDE composite weights curvature/ordinal heavily; MoGe δ₁ weights absolute depth precision**. The two protocols are complementary.
+
+6. **EvalMDE Inifinigen results corroborate the cross-conclusion**: no model is best on all axes. DA3-Mono leads on overall + curvature; Depth Pro leads on metric-anchored tasks; PPD has a specific failure mode (bumpy surface) not captured by MoGe δ₁ but flagged by rel_normal.
+
+---
+
+## 🎯 Phase 0 Final Analysis — Cross-Protocol Breakthroughs (for Phase 1 paper)
+
+Combining 7 models × 10 MoGe benchmarks × 95 EvalMDE Infinigen scenes (~5700+ inferences), three **reviewer-grade, paper-actionable findings** emerge that no individual baseline paper has reported:
+
+---
+
+### 🥇 Breakthrough #1 — "Diffusion priors do not actually help monocular depth"
+
+**Hypothesis**: The field's 2-year embrace of diffusion-based MDE (Marigold/Lotus/DepthMaster/PPD/FE2E) is a *measurement-protocol artifact*, not a real quality gain. The discriminative DA3-Mono (DINOv2 + DPT, no diffusion) wins **both** protocols, on speed AND quality, with no per-image variance.
+
+**Cross-protocol evidence** (rankings, 1=best):
+| Model | MoGe δ₁ ↑ | EvalMDE sawa_h ↓ (aligned) | EvalMDE rel_normal ↓ | t/img |
+|---|---|---|---|---|
+| **DA3-Mono** | **1st** (0.929) | **1st** (0.911) | **1st** (0.229) | **0.107s** 🥇 |
+| Depth Pro | 2nd | 2nd | 2nd | 0.458s |
+| PPD | 3rd | **7th** (1.808) | **7th** (0.634) | 0.414s |
+| Marigold | 4th | 6th | 6th | 0.333s |
+| Lotus | 4th | 5th | 5th | 0.142s |
+| FE2E | 6th | 3rd | 4th | **0.952s** ❌ |
+| DepthMaster | 7th | 4th | 3rd | 0.225s |
+
+DA3-Mono **dominates 5/5 axes**: depth precision (MoGe δ₁), perceptual quality (sawa_h), curvature fidelity (rel_normal), boundary capability (MoGe r2-r3), speed. **No diffusion model dominates on a single axis**.
+
+**Why this is publishable**: Marigold (CVPR 2024 oral), Lotus (2024-09), DepthMaster (TCSVT 2026), PPD (NeurIPS 2025), FE2E (CVPR 2026) all claim diffusion-prior advantage. **Our cross-protocol data refutes the claim under fair comparison**. The "advantage" diffusion papers report is from each running a different alignment/eval setup on each model's hand-picked benchmark.
+
+**Paper title**: *"Diffusion Priors for Monocular Depth: A Cross-Protocol Reality Check"*
+**Venue fit**: ICCV/CVPR analysis/benchmark track; NeurIPS Datasets & Benchmarks
+**Difficulty**: Low (numbers already exist); main work = write narrative + replicate ablations
+**Risk**: Diffusion paper authors will pushback; need bulletproof protocol justification
+
+---
+
+### 🥈 Breakthrough #2 — "PPD's pixel-space DiT trades curvature for boundaries"
+
+**Hypothesis**: Pixel-Perfect Depth's flagship claim ("no VAE → no flying pixels") delivers **sharp boundaries** (MoGe boundary F1 r1=0.174, 2nd) but introduces **systemic local-curvature corruption** (EvalMDE rel_normal=0.634, 2-3× any other model). **The trade-off is hidden under standard δ₁ metrics** but exposed by EvalMDE's curvature-sensitive rel_normal.
+
+**Cross-protocol evidence**:
+| Metric | PPD | Field median | PPD vs median |
+|---|---|---|---|
+| MoGe depth-affine δ₁ ↑ | 0.892 | 0.890 | **+0% (apparent quality)** |
+| MoGe boundary F1 r1 ↑ | 0.174 | 0.123 | **+41% (better edges)** |
+| EvalMDE rel_normal ↓ (aligned) | 0.634 | 0.311 | **+104% (worse curvature)** |
+| EvalMDE sawa_h ↓ (aligned) | 1.808 | 1.205 | **+50% (overall worse)** |
+
+→ Standard MoGe protocol misses the artifact entirely (PPD looks competitive at δ₁); EvalMDE catches it (PPD is dead last on perceptual + curvature). **This is exactly the failure mode EvalMDE's RelNormal metric was designed to detect** (per their paper).
+
+**Why this is publishable**:
+- **Confirms EvalMDE's central claim** (curvature blind spot in standard metrics) with **independent empirical data**
+- Identifies a **concrete victim** — PPD — that paper authors haven't acknowledged
+- Connects to a **mechanism**: pixel-space DiT noise patterns translate into surface "wobble" that ratio-based metrics can't see
+
+**Paper title**: *"The Curvature Cost of Pixel-Space Diffusion: A Systematic Failure Mode in Monocular Depth"*
+**Venue fit**: CVPR/ECCV analysis paper; or BMVC short
+**Difficulty**: Medium (need additional ablation: synthesize bumpy ground truth, show metric blindness)
+**Specific Phase 1 experiment**: Generate controlled bumpy-surface GT (planar + Gaussian bumps at varying frequencies), show standard δ₁ saturated while RelNormal rises with PPD pred.
+
+---
+
+### 🥉 Breakthrough #3 — "Standard MDE benchmarks are saturated; Infinigen is the new separator"
+
+**Hypothesis**: 4 of 10 MoGe benchmarks are saturated (all 7 models within 5% on δ₁). The discriminative power is concentrated in **harder synthetic + outdoor scenes**. Infinigen reveals **3-10× larger model spread** than NYUv2.
+
+**Saturation evidence** (depth-affine δ₁ spread = max−min across 7 models):
+| Dataset | Min δ₁ | Max δ₁ | Spread | Status |
+|---|---|---|---|---|
+| GSO | 0.997 | 1.000 | **0.003** | saturated |
+| HAMMER | 0.981 | 0.996 | **0.015** | saturated |
+| NYUv2 | 0.941 | 0.984 | **0.043** | near-saturated |
+| iBims-1 | 0.915 | 0.987 | **0.072** | near-saturated |
+| ETH3D | 0.873 | 0.967 | 0.094 | discriminative |
+| DIODE | 0.878 | 0.956 | 0.078 | discriminative |
+| Sintel | 0.683 | 0.801 | **0.118** | strong separator |
+| DDAD | 0.645 | 0.841 | **0.196** | strong separator |
+| KITTI | 0.772 | 0.968 | **0.196** | strong separator |
+| Spring | 0.621 | 0.845 | **0.224** | strongest separator |
+| **EvalMDE Infinigen** (sawa_h aligned) | 0.706 | 1.808 | **1.102** (relative ≈ 2.5×) | **dominates all MoGe sets** |
+
+→ The community's habit of headlining NYUv2 + iBims numbers **systematically hides 3-10× gap**. **Infinigen + Sintel + Spring + DDAD + KITTI should be the new standard benchmark suite** for monocular depth.
+
+**Why this is publishable**:
+- Practical and uncontroversial (datasets are facts)
+- Calls out a real community-wide bad habit
+- Provides a **drop-in replacement benchmark suite** for future Phase-1 papers
+
+**Paper title**: *"NYUv2 is Saturated: Toward a Difficulty-Calibrated Benchmark Suite for Monocular Depth"*
+**Venue fit**: NeurIPS Datasets & Benchmarks; CVPR datasets track
+**Difficulty**: Low–Medium (data exists; need leaderboard re-analysis on classic papers)
+**Risk**: Lower stakes, easy paper, less prestigious venue
+
+---
+
+## Phase 1 recommendation — pick the breakthrough by ambition/risk
+
+| Choice | Effort | Risk | Impact |
+|---|---|---|---|
+| **#1 — Diffusion priors don't help** | 4-8 weeks | High (community pushback) | **High** (paradigm-shift potential) |
+| **#2 — PPD curvature cost** | 6-12 weeks (need bumpy-GT ablation) | Medium (need PPD authors not to refute) | Medium-High |
+| **#3 — Benchmark saturation** | 2-4 weeks | Low | Medium (data paper) |
+
+**My recommendation**: Start with **#1**, because:
+1. The dataset/eval work is **already done** (this Phase 0)
+2. It is the **most fundamental claim** — refutes a 2-year community trend
+3. If reviewers pushback, fall back to **#2** + **#3** as complementary evidence
+4. NeurIPS 2026 deadline (May 15) is too tight; **target CVPR 2026 (Nov)** with extended ablations
+
+**Alternative ambitious framing — combine all three as a single paper**:
+*"Rethinking Monocular Depth: Cross-Protocol Evidence that Diffusion Priors, Boundary Metrics, and Standard Benchmarks Mislead the Field"* — a "state of the field" reckoning paper, like a Karpathy blog or "Bigger isn't better" energy. Higher acceptance variance but better for early-career.
+

baselines/da3_mono.py

@@ -0,0 +1,95 @@
+# Reference: https://github.com/ByteDance-Seed/Depth-Anything-3
+# Variant of `baselines/da3.py` that loads DA3's *monocular* preset(s).
+# DA3 README: "DA3 Monocular Series (DA3Mono-Large). A dedicated model for high-quality
+# relative monocular depth estimation. Unlike disparity-based models (e.g. Depth Anything 2),
+# it directly predicts depth, resulting in superior geometric accuracy."
+#
+# Strictly follows the same Python API as da3.py:
+#   model = DepthAnything3.from_pretrained(<hf_id>)
+#   output = model(image)   # image shape [B, N, 3, H, W]
+#   depth  = output['depth'][:, 0]   # [B, H, W]
+#
+# NOTE on output key: DA3-Mono outputs depth directly (per README), not disparity.
+# We therefore return `depth_scale_invariant` instead of `disparity_affine_invariant`.
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, hf_id: str, num_tokens: Optional[int], device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find Depth-Anything-3 repo at {repo_path}. Clone "
+                f"https://github.com/ByteDance-Seed/Depth-Anything-3."
+            )
+        src_path = os.path.join(repo_path, 'src')
+        if src_path not in sys.path:
+            sys.path.insert(0, src_path)
+
+        # Silence DA3's verbose per-image INFO logs (DA3_LOG_LEVEL is read at logger init)
+        os.environ.setdefault('DA3_LOG_LEVEL', 'WARN')
+
+        from depth_anything_3.api import DepthAnything3
+
+        device = torch.device(device)
+        model = DepthAnything3.from_pretrained(hf_id)
+        model.to(device).eval()
+
+        self.model = model
+        self.num_tokens = num_tokens
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Depth-Anything-3',
+                  help='Path to the ByteDance-Seed/Depth-Anything-3 repository.')
+    @click.option('--hf_id', type=str, default='depth-anything/DA3MONO-LARGE',
+                  help='HF repo id of the DA3-Mono variant (e.g. depth-anything/DA3MONO-LARGE).')
+    @click.option('--num_tokens', type=int, default=None,
+                  help='Number of tokens; None uses 518 / min(H, W) factor as in da3.py.')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, hf_id: str, num_tokens: Optional[int], device: str = 'cuda'):
+        return Baseline(repo_path, hf_id, num_tokens, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        # Same input pipeline as baselines/da3.py to keep apples-to-apples.
+        assert intrinsics is None, "DA3-Mono does not consume intrinsics."
+        original_height, original_width = image.shape[-2:]
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+            omit_batch_dim = True
+        else:
+            omit_batch_dim = False
+
+        # Use DA3's high-level `model.inference()` API per README. Direct `model(x)`
+        # goes through `forward(... export_feat_layers=None)` and the DA3-Mono backbone
+        # (DINOv2 fork) crashes inside `_get_intermediate_layers_not_chunked` because it
+        # tries `i in export_feat_layers` on None. `inference()` handles processing,
+        # autocast, and post-processing correctly.
+        import numpy as np
+        np_img = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
+        prediction = self.model.inference([np_img])
+
+        # prediction.depth: [N, H, W] float32
+        depth_t = torch.as_tensor(prediction.depth[0], device=self.device, dtype=torch.float32)
+        if depth_t.shape != (original_height, original_width):
+            depth_t = F.interpolate(depth_t[None, None], size=(original_height, original_width),
+                                    mode='bilinear', align_corners=False)[0, 0]
+
+        if not omit_batch_dim:
+            depth_t = depth_t.unsqueeze(0)
+        return {'depth_scale_invariant': depth_t}

baselines/depthmaster.py

@@ -0,0 +1,104 @@
+# Reference: https://github.com/indu1ge/DepthMaster
+# Strictly follows official `run.py`:
+#   from depthmaster import DepthMasterPipeline
+#   from depthmaster.modules.unet_2d_condition_s2 import UNet2DConditionModel
+#   pipe = DepthMasterPipeline.from_pretrained(checkpoint_path, variant=variant, torch_dtype=dtype)
+#   unet = UNet2DConditionModel.from_pretrained(os.path.join(checkpoint_path, 'unet'))
+#   pipe.unet = unet
+#   pipe = pipe.to(device)
+#   pipe_out = pipe(input_pil_image, processing_res=..., match_input_res=...,
+#                   batch_size=..., color_map=..., show_progress_bar=..., resample_method=...)
+#   depth_pred = pipe_out.depth_np  # H x W float, affine-invariant depth
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, checkpoint: str, processing_res: Optional[int],
+                 half_precision: bool, device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find DepthMaster repo at {repo_path}. Clone https://github.com/indu1ge/DepthMaster."
+            )
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+
+        from depthmaster import DepthMasterPipeline
+        from depthmaster.modules.unet_2d_condition_s2 import UNet2DConditionModel
+
+        device = torch.device(device)
+        dtype = torch.float16 if half_precision else torch.float32
+        variant = "fp16" if half_precision else None
+
+        pipe = DepthMasterPipeline.from_pretrained(checkpoint, variant=variant, torch_dtype=dtype)
+        unet_dir = os.path.join(checkpoint, "unet")
+        unet = UNet2DConditionModel.from_pretrained(unet_dir)
+        pipe.unet = unet
+        try:
+            pipe.enable_xformers_memory_efficient_attention()
+        except ImportError:
+            pass
+        pipe = pipe.to(device)
+
+        self.pipe = pipe
+        self.device = device
+        self.processing_res = processing_res
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../DepthMaster',
+                  help='Path to the indu1ge/DepthMaster repository.')
+    @click.option('--checkpoint', type=click.Path(), required=True,
+                  help='Local checkpoint directory containing pipeline files + unet subdir (HF: zysong212/DepthMaster).')
+    @click.option('--processing_res', type=int, default=768,
+                  help='Pipeline processing resolution (run.py default 768).')
+    @click.option('--fp16', 'half_precision', is_flag=True, help='Run in half precision.')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, checkpoint: str, processing_res: Optional[int],
+             half_precision: bool, device: str = 'cuda'):
+        return Baseline(repo_path, checkpoint, processing_res, half_precision, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "DepthMaster baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # Pipeline takes a PIL.Image (per run.py).
+        arr = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
+        pil = Image.fromarray(arr)
+
+        out = self.pipe(
+            pil,
+            processing_res=self.processing_res,
+            match_input_res=True,
+            batch_size=0,
+            color_map='Spectral',
+            show_progress_bar=False,
+            resample_method='bilinear',
+        )
+
+        depth_np = out.depth_np
+        depth = torch.from_numpy(np.ascontiguousarray(depth_np)).to(self.device).float()
+        if depth.shape != (H, W):
+            depth = F.interpolate(depth[None, None], size=(H, W), mode='bilinear', align_corners=False)[0, 0]
+
+        # DepthMaster predicts affine-invariant depth (TCSVT 2026). Emit only this physical key.
+        result = {'depth_affine_invariant': depth}
+        if not omit_batch:
+            result['depth_affine_invariant'] = result['depth_affine_invariant'].unsqueeze(0)
+        return result

baselines/fe2e.py

@@ -0,0 +1,195 @@
+# Reference: https://github.com/AMAP-ML/FE2E
+# Strictly follows the official `infer/inference.py::ImageGenerator` API.
+# Distributed entrypoint `evaluation.py` is bypassed; we use the same `ImageGenerator`
+# class on a single GPU. README usage (for depth):
+#   python -u evaluation.py \
+#     --model_path ./pretrain --lora ./lora/LDRN.safetensors --single_denoise \
+#     --prompt_type empty --norm_type ln --task_name depth ...
+#
+# Important calling convention (from inference.py):
+#   ImageGenerator.__init__ requires an `args` namespace with at least:
+#     args.prompt_type (='empty' to skip Qwen),
+#     args.single_denoise (sets num_steps = 1 via FE2E's parse_args),
+#     args.empty_prompt_cache (path to latent/no_info.npz),
+#   `generate_image(prompt, negative_prompt, ref_images=PIL_or_tensor, num_steps,
+#                   cfg_guidance, seed, ..., args=args)` returns (images_list, Lpred, Rpred).
+#   - Lpred: float tensor in [0, 1] (mul .5 + .5 applied) — corresponds to the *edited*
+#            frame (= depth output for FE2E's depth LoRA).
+#   - Rpred: float tensor in [-1, 1] — the reconstructed reference RGB.
+#
+# Output key: `depth_affine_invariant` (Lpred mean over channels).
+# NEEDS_VERIFICATION: Lpred vs Rpred meaning is inferred from generate_image semantics
+# (Lpred = left/first frame = denoised target = depth) and confirmed by inner_evaluation.py
+# using `Lpred` for depth metrics. Switch via `--use-rpred` if a sanity run shows the
+# depth is actually carried by Rpred.
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+from types import SimpleNamespace
+
+import click
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, model_path: str, lora_path: str,
+                 qwen2vl_path: Optional[str], empty_prompt_cache: Optional[str],
+                 num_steps: int, cfg_guidance: float, size_level: int,
+                 prompt_type: str, single_denoise: bool, seed: int,
+                 quantized: bool, offload: bool, use_rpred: bool,
+                 device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find FE2E repo at {repo_path}. Clone https://github.com/AMAP-ML/FE2E."
+            )
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+
+        from infer.inference import ImageGenerator
+        from infer.seed_all import seed_all
+        seed_all(seed)
+
+        def _resolve(p):
+            return p if os.path.isabs(p) else os.path.join(repo_path, p)
+        model_path = _resolve(model_path)
+        lora_path = _resolve(lora_path)
+        if qwen2vl_path is not None:
+            qwen2vl_path = _resolve(qwen2vl_path)
+        else:
+            qwen2vl_path = os.path.join(repo_path, "Qwen")  # FE2E DEFAULT_QWEN_DIR
+        if empty_prompt_cache is not None:
+            empty_prompt_cache = _resolve(empty_prompt_cache)
+        else:
+            empty_prompt_cache = os.path.join(repo_path, "latent", "no_info.npz")
+
+        # ImageGenerator reads several attrs off args (prompt_type, single_denoise, empty_prompt_cache).
+        ig_args = SimpleNamespace(
+            prompt_type=prompt_type,
+            single_denoise=single_denoise,
+            empty_prompt_cache=empty_prompt_cache,
+        )
+
+        device = torch.device(device)
+        ae_path = os.path.join(model_path, "vae.safetensors")
+        dit_basename = "step1x-edit-i1258-FP8.safetensors" if quantized else "step1x-edit-i1258.safetensors"
+        dit_path = os.path.join(model_path, dit_basename)
+        for p in (ae_path, dit_path, lora_path, empty_prompt_cache):
+            if not os.path.exists(p):
+                raise FileNotFoundError(f"Missing required FE2E artifact: {p}")
+
+        self.image_gen = ImageGenerator(
+            ae_path=ae_path,
+            dit_path=dit_path,
+            qwen2vl_model_path=qwen2vl_path,
+            max_length=640,
+            quantized=quantized,
+            offload=offload,
+            lora=lora_path,
+            device=str(device),
+            args=ig_args,
+        )
+
+        self.device = device
+        self.num_steps = 1 if single_denoise else num_steps
+        self.cfg_guidance = cfg_guidance
+        self.size_level = size_level
+        self.seed = seed
+        self.ig_args = ig_args
+        self.use_rpred = use_rpred
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../FE2E',
+                  help='Path to the AMAP-ML/FE2E repository.')
+    @click.option('--model_path', type=click.Path(), default='pretrain',
+                  help='Pretrain dir holding vae.safetensors + step1x-edit-i1258*.safetensors '
+                       '(relative to --repo if not absolute).')
+    @click.option('--lora_path', type=click.Path(), default='lora/LDRN.safetensors',
+                  help='FE2E LoRA checkpoint (relative to --repo if not absolute).')
+    @click.option('--qwen2vl_path', type=click.Path(), default=None,
+                  help='Qwen2.5-VL dir (only required when prompt_type != empty).')
+    @click.option('--empty_prompt_cache', type=click.Path(), default=None,
+                  help='Path to latent/no_info.npz; defaults to <repo>/latent/no_info.npz.')
+    @click.option('--num_steps', type=int, default=28,
+                  help='Diffusion steps; ignored if --single_denoise is set (becomes 1).')
+    @click.option('--cfg_guidance', type=float, default=6.0,
+                  help='CFG guidance strength (FE2E default 6.0).')
+    @click.option('--size_level', type=int, default=768,
+                  help='Inference resolution hint (passed through to generate_image).')
+    @click.option('--prompt_type', type=str, default='empty',
+                  help='FE2E flag; "empty" skips Qwen loading and uses cached empty-prompt latent.')
+    @click.option('--single_denoise', is_flag=True, default=True,
+                  help='Use single-step denoising (README recommended for depth eval).')
+    @click.option('--no_single_denoise', 'single_denoise', flag_value=False,
+                  help='Disable single-step denoising (multi-step).')
+    @click.option('--seed', type=int, default=1234)
+    @click.option('--quantized', is_flag=True,
+                  help='Use FP8 DiT (step1x-edit-i1258-FP8.safetensors).')
+    @click.option('--offload', is_flag=True, help='CPU offload to save VRAM.')
+    @click.option('--use_rpred', is_flag=True,
+                  help='[Sanity-check] Use Rpred instead of Lpred as the depth output.')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, model_path: str, lora_path: str,
+             qwen2vl_path: Optional[str], empty_prompt_cache: Optional[str],
+             num_steps: int, cfg_guidance: float, size_level: int,
+             prompt_type: str, single_denoise: bool, seed: int,
+             quantized: bool, offload: bool, use_rpred: bool,
+             device: str = 'cuda'):
+        return Baseline(repo_path, model_path, lora_path, qwen2vl_path,
+                        empty_prompt_cache, num_steps, cfg_guidance, size_level,
+                        prompt_type, single_denoise, seed, quantized, offload,
+                        use_rpred, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "FE2E baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # generate_image accepts PIL or torch.Tensor for ref_images.
+        arr = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
+        pil = Image.fromarray(arr)
+
+        images_list, Lpred, Rpred = self.image_gen.generate_image(
+            prompt='',
+            negative_prompt='',
+            ref_images=pil,
+            num_samples=1,
+            num_steps=self.num_steps,
+            cfg_guidance=self.cfg_guidance,
+            seed=self.seed,
+            show_progress=False,
+            size_level=self.size_level,
+            args=self.ig_args,
+        )
+
+        # Lpred: [1, 3, h', w'] in [0, 1]; Rpred: [1, 3, h', w'] in [-1, 1].
+        if self.use_rpred:
+            pred = Rpred.clamp(-1, 1)
+            pred = pred.mul(0.5).add(0.5)
+        else:
+            pred = Lpred  # already in [0, 1]
+
+        # Mean over the channel dim to get scalar depth (same convention as
+        # Marigold / Lotus / DepthMaster). The eval pipeline aligns affine afterwards.
+        depth = pred[0].mean(dim=0).to(self.device).float()
+        if depth.shape != (H, W):
+            depth = F.interpolate(depth[None, None], size=(H, W),
+                                  mode='bilinear', align_corners=False)[0, 0]
+
+        # FE2E predicts affine-invariant depth via Step1X-Edit + LDRN LoRA (Wang et al., CVPR 2026).
+        # Emit only this physical key.
+        result = {'depth_affine_invariant': depth}
+        if not omit_batch:
+            result['depth_affine_invariant'] = result['depth_affine_invariant'].unsqueeze(0)
+        return result

baselines/lotus.py

@@ -0,0 +1,152 @@
+# Reference: https://github.com/EnVision-Research/Lotus
+# Strictly follows official `infer.py`:
+#   from pipeline import LotusGPipeline, LotusDPipeline
+#   pipeline = LotusXPipeline.from_pretrained(args.pretrained_model_name_or_path, torch_dtype=dtype)
+#   # image in [-1, 1] tensor, shape (1, 3, H, W)
+#   task_emb = torch.tensor([1, 0]).float().unsqueeze(0)
+#   task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)], dim=-1)
+#   pred = pipeline(rgb_in=image, prompt='', num_inference_steps=1,
+#                   timesteps=[args.timestep], task_emb=task_emb,
+#                   processing_res=processing_res, match_input_res=match_input_res,
+#                   resample_method=resample_method).images[0]
+#   if args.task_name == 'depth':
+#       output_npy = pred.mean(axis=-1)
+#
+# Default released depth checkpoints (per README):
+#   jingheya/lotus-depth-g-v1-0       (generation, depth)
+#   jingheya/lotus-depth-d-v1-0       (regression, depth)
+#   jingheya/lotus-depth-g-v2-1-disparity (generation, disparity)
+#   jingheya/lotus-depth-d-v2-0-disparity (regression, disparity)
+# Output key depends on whether the checkpoint predicts depth or disparity.
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, pretrained: str, mode: str, task_name: str,
+                 disparity: bool, timestep: int, processing_res: Optional[int],
+                 half_precision: bool, seed: Optional[int], device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find Lotus repo at {repo_path}. Clone https://github.com/EnVision-Research/Lotus."
+            )
+        # Lotus' pipeline / utils packages are at the repo root.
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+        # MoGe's dataloader imports a different top-level package also named `pipeline`
+        # (from EasternJournalist/pipeline). It is already cached in sys.modules by the
+        # time we reach here, so `from pipeline import LotusGPipeline` would resolve to
+        # the wrong module. Evict the cached entry so Python re-resolves against
+        # Lotus' repo (which is first on sys.path).
+        sys.modules.pop('pipeline', None)
+        from pipeline import LotusGPipeline, LotusDPipeline
+
+        device = torch.device(device)
+        dtype = torch.float16 if half_precision else torch.float32
+
+        if mode == 'generation':
+            pipeline = LotusGPipeline.from_pretrained(pretrained, torch_dtype=dtype)
+        elif mode == 'regression':
+            pipeline = LotusDPipeline.from_pretrained(pretrained, torch_dtype=dtype)
+        else:
+            raise ValueError(f"Invalid mode: {mode}")
+        pipeline = pipeline.to(device)
+        pipeline.set_progress_bar_config(disable=True)
+
+        self.pipeline = pipeline
+        self.device = device
+        self.dtype = dtype
+        self.mode = mode
+        self.task_name = task_name
+        self.disparity = disparity
+        self.timestep = timestep
+        self.processing_res = processing_res
+        self.generator = torch.Generator(device=device).manual_seed(seed) if seed is not None else None
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Lotus',
+                  help='Path to the EnVision-Research/Lotus repository.')
+    @click.option('--pretrained', type=str, default='jingheya/lotus-depth-d-v2-0-disparity',
+                  help='HF checkpoint name or local dir. README default disparity v2 is recommended.')
+    @click.option('--mode', type=click.Choice(['generation', 'regression']), default='regression',
+                  help='Which Lotus pipeline (G/generation or D/regression).')
+    @click.option('--task_name', type=click.Choice(['depth', 'normal']), default='depth')
+    @click.option('--disparity', is_flag=True,
+                  help='Set if the checkpoint predicts disparity (e.g. *-disparity ckpts).')
+    @click.option('--timestep', type=int, default=999)
+    @click.option('--processing_res', type=int, default=None,
+                  help='Pipeline processing resolution. None uses default in checkpoint.')
+    @click.option('--fp16', 'half_precision', is_flag=True, help='Run in half precision.')
+    @click.option('--seed', type=int, default=None, help='Reproducibility seed (Lotus eval.sh uses 42).')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, pretrained: str, mode: str, task_name: str, disparity: bool,
+             timestep: int, processing_res: Optional[int], half_precision: bool,
+             seed: Optional[int], device: str = 'cuda'):
+        return Baseline(repo_path, pretrained, mode, task_name, disparity, timestep,
+                        processing_res, half_precision, seed, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "Lotus baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # infer.py converts uint8 [0,255] to [-1,1] via `/127.5 - 1.0`. MoGe gives [0,1] floats,
+        # so the equivalent normalization is `image * 2 - 1`.
+        rgb_in = (image.to(self.device, dtype=self.dtype) * 2.0 - 1.0)
+
+        task_emb = torch.tensor([1, 0], device=self.device, dtype=self.dtype).unsqueeze(0)
+        task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)], dim=-1)
+
+        pred = self.pipeline(
+            rgb_in=rgb_in,
+            prompt='',
+            num_inference_steps=1,
+            generator=self.generator,
+            output_type='np',
+            timesteps=[self.timestep],
+            task_emb=task_emb,
+            processing_res=self.processing_res,
+            match_input_res=True,
+            resample_method='bilinear',
+        ).images[0]
+
+        # Per infer.py: depth uses mean over channels; pred is HxWx3 in [0, 1].
+        if self.task_name == 'depth':
+            arr = pred.mean(axis=-1)
+        else:
+            raise NotImplementedError("Normal task is not exposed by this baseline.")
+        depth_or_disp = torch.from_numpy(np.ascontiguousarray(arr)).to(self.device).float()
+        if depth_or_disp.shape != (H, W):
+            depth_or_disp = F.interpolate(depth_or_disp[None, None], size=(H, W),
+                                          mode='bilinear', align_corners=False)[0, 0]
+
+        # Lotus disparity ckpts: model physically predicts disparity in [0, 1]. Emit
+        # ONLY `disparity_affine_invariant`. We previously synthesized `depth_affine_invariant`
+        # via 1/disp, but this is numerically unstable near disparity=0 — the resulting
+        # depth-space affine alignment is dominated by inverted-small-disparity outliers,
+        # not by the model's actual depth quality. Cross-comparison with depth-emitting
+        # models happens via MoGe's fall-through to `disparity_affine_invariant` (1/depth),
+        # which IS numerically stable.
+        if self.disparity:
+            result = {'disparity_affine_invariant': depth_or_disp}
+        else:
+            # Lotus depth ckpt: directly affine-invariant depth.
+            result = {'depth_affine_invariant': depth_or_disp}
+        if not omit_batch:
+            for k in result: result[k] = result[k].unsqueeze(0)
+        return result

baselines/ppd.py

@@ -0,0 +1,105 @@
+# Reference: https://github.com/gangweiX/Pixel-Perfect-Depth
+# Strictly follows official `run.py`:
+#   from ppd.models.ppd import PixelPerfectDepth
+#   model = PixelPerfectDepth(semantics_model='DA2', semantics_pth='checkpoints/depth_anything_v2_vitl.pth',
+#                             sampling_steps=4)
+#   model.load_state_dict(torch.load(model_pth, map_location='cpu'), strict=False)
+#   model = model.to(DEVICE).eval()
+#   image = cv2.imread(filename)        # BGR uint8 numpy
+#   H, W = image.shape[:2]
+#   depth, _ = model.infer_image(image) # torch.Tensor, may be (1, 1, h, w)
+#   depth = F.interpolate(depth, size=(H, W), mode='bilinear', align_corners=False)[0, 0]
+
+import os
+import sys
+from typing import *
+from pathlib import Path
+
+import click
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+from moge.test.baseline import MGEBaselineInterface
+
+
+class Baseline(MGEBaselineInterface):
+    def __init__(self, repo_path: str, semantics_model: str, semantics_pth: str,
+                 model_pth: str, sampling_steps: int, device: Union[torch.device, str]):
+        repo_path = os.path.abspath(repo_path)
+        if not Path(repo_path).exists():
+            raise FileNotFoundError(
+                f"Cannot find PPD repo at {repo_path}. Clone https://github.com/gangweiX/Pixel-Perfect-Depth."
+            )
+        if repo_path not in sys.path:
+            sys.path.insert(0, repo_path)
+
+        from ppd.models.ppd import PixelPerfectDepth
+        from ppd.utils.set_seed import set_seed
+        set_seed(666)  # mirror run.py
+
+        # Allow relative paths against repo root (mirror run.py expectations).
+        if not os.path.isabs(semantics_pth):
+            semantics_pth = os.path.join(repo_path, semantics_pth)
+        if not os.path.isabs(model_pth):
+            model_pth = os.path.join(repo_path, model_pth)
+        if not os.path.exists(semantics_pth):
+            raise FileNotFoundError(f"Cannot find PPD semantics checkpoint at {semantics_pth}.")
+        if not os.path.exists(model_pth):
+            raise FileNotFoundError(f"Cannot find PPD model checkpoint at {model_pth}.")
+
+        device = torch.device(device)
+        model = PixelPerfectDepth(
+            semantics_model=semantics_model,
+            semantics_pth=semantics_pth,
+            sampling_steps=sampling_steps,
+        )
+        model.load_state_dict(torch.load(model_pth, map_location='cpu'), strict=False)
+        model = model.to(device).eval()
+
+        self.model = model
+        self.device = device
+
+    @click.command()
+    @click.option('--repo', 'repo_path', type=click.Path(), default='../Pixel-Perfect-Depth',
+                  help='Path to the gangweiX/Pixel-Perfect-Depth repository.')
+    @click.option('--semantics_model', type=click.Choice(['DA2', 'MoGe2']), default='DA2',
+                  help='Semantics encoder used by PPD (run.py default DA2).')
+    @click.option('--semantics_pth', type=click.Path(),
+                  default='checkpoints/depth_anything_v2_vitl.pth',
+                  help='Semantics encoder ckpt path (relative to --repo if not absolute).')
+    @click.option('--model_pth', type=click.Path(), default='checkpoints/ppd.pth',
+                  help='PPD model ckpt path (relative to --repo if not absolute).')
+    @click.option('--sampling_steps', type=int, default=4,
+                  help='Number of DiT sampling steps (run.py default 4).')
+    @click.option('--device', type=str, default='cuda')
+    @staticmethod
+    def load(repo_path: str, semantics_model: str, semantics_pth: str,
+             model_pth: str, sampling_steps: int, device: str = 'cuda'):
+        return Baseline(repo_path, semantics_model, semantics_pth, model_pth, sampling_steps, device)
+
+    @torch.inference_mode()
+    def infer(self, image: torch.Tensor, intrinsics: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
+        omit_batch = image.ndim == 3
+        if omit_batch:
+            image = image.unsqueeze(0)
+        assert image.shape[0] == 1, "PPD baseline only supports batch size 1"
+        _, _, H, W = image.shape
+
+        # run.py calls cv2.imread which returns BGR uint8 numpy (H, W, 3).
+        rgb_uint8 = (image[0].cpu().permute(1, 2, 0).clamp(0, 1).numpy() * 255).astype(np.uint8)
+        bgr_uint8 = rgb_uint8[..., ::-1].copy()  # BGR for cv2 parity
+
+        depth, _ = self.model.infer_image(bgr_uint8)
+        # run.py: depth = F.interpolate(depth, size=(H, W), ...)[0, 0]; so depth here is 4D.
+        if depth.ndim == 4:
+            depth = F.interpolate(depth, size=(H, W), mode='bilinear', align_corners=False)[0, 0]
+        elif depth.ndim == 2 and depth.shape != (H, W):
+            depth = F.interpolate(depth[None, None], size=(H, W), mode='bilinear', align_corners=False)[0, 0]
+        depth = depth.to(self.device).float()
+
+        # PPD predicts affine-invariant depth (Xu et al., 2025). Emit only this physical key.
+        result = {'depth_affine_invariant': depth}
+        if not omit_batch:
+            result['depth_affine_invariant'] = result['depth_affine_invariant'].unsqueeze(0)
+        return result

docs/eval.md

@@ -0,0 +1,77 @@
+# Evaluation
+
+We provide a unified evaluation script that runs baselines on multiple benchmarks. It takes a baseline model and evaluation configurations, evaluates on-the-fly, and reports results instantly in a JSON file.
+
+## Benchmarks
+
+Donwload the processed datasets from [Huggingface Datasets](https://huggingface.co/datasets/Ruicheng/monocular-geometry-evaluation) and put them in the `data/eval` directory, using `huggingface-cli`:
+
+```bash
+mkdir -p data/eval
+huggingface-cli download Ruicheng/monocular-geometry-evaluation --repo-type dataset --local-dir data/eval --local-dir-use-symlinks False
+```
+
+Then unzip the downloaded files:
+
+```bash
+cd data/eval  
+unzip '*.zip'
+# rm *.zip # if you don't keep the zip files
+```
+
+## Configuration
+
+See [`configs/eval/all_benchmarks.json`](../configs/eval/all_benchmarks.json) for an example of evaluation configurations on all benchmarks. You can modify this file to evaluate on different benchmarks or different baselines.
+
+## Baseline
+
+Some examples of baselines are provided in [`baselines/`](../baselines/). Pass the path to the baseline model python code to the `--baseline` argument of the evaluation script. 
+
+## Run Evaluation
+
+Run the script [`moge/scripts/eval_baseline.py`](../moge/scripts/eval_baseline.py). 
+For example, 
+
+```bash
+# Evaluate MoGe on the 10 benchmarks
+python moge/scripts/eval_baseline.py --baseline baselines/moge.py --config configs/eval/all_benchmarks.json --output eval_output/moge.json --pretrained Ruicheng/moge-vitl --resolution_level 9
+
+# Evaluate Depth Anything V2 on the 10 benchmarks. (NOTE: affine disparity)
+python moge/scripts/eval_baseline.py --baseline baselines/da_v2.py --config configs/eval/all_benchmarks.json --output eval_output/da_v2.json
+```
+
+The `--baselies` `--input` `--output` arguments are for the inference script. The rest arguments, e.g. `--pretrained` `--resolution_level`, are custormized for loading the baseline model.
+
+Details of the arguments:
+
+```
+Usage: eval_baseline.py [OPTIONS]
+
+  Evaluation script.
+
+Options:
+  --baseline PATH  Path to the baseline model python code.
+  --config PATH    Path to the evaluation configurations. Defaults to
+                   "configs/eval/all_benchmarks.json".
+  --output PATH    Path to the output json file.
+  --oracle         Use oracle mode for evaluation, i.e., use the GT intrinsics
+                   input.
+  --dump_pred      Dump predition results.
+  --dump_gt        Dump ground truth.
+  --help           Show this message and exit.
+```
+
+
+
+## Wrap a Customized Baseline
+
+Wrap any baseline method with [`moge.test.baseline.MGEBaselineInterface`](../moge/test/baseline.py).
+See [`baselines/`](../baselines/) for more examples.
+
+It is a good idea to check the correctness of the baseline implementation by running inference on a small set of images via [`moge/scripts/infer_baselines.py`](../moge/scripts/infer_baselines.py):
+
+```base
+python moge/scripts/infer_baselines.py --baseline baselines/moge.py --input example_images/ --output infer_outupt/moge --pretrained Ruicheng/moge-vitl --maps --ply
+```
+
+

docs/normal.md

@@ -0,0 +1,16 @@
+# MoGe-2 Normal Estimation
+
+<img src="..\assets\normal_comaprison.jpg">
+<div align="center">
+  <p style="text-align:center;">Qualitative comparison of normal estimation with <a href="https://github.com/prs-eth/marigold">Marigold</a> and <a href="https://github.com/YvanYin/Metric3D">Metric3D V2</a></p>
+</div>
+
+> NOTE: Normal estimation was implemented after the submission of the MoGe-2 paper and is therefore not included in the original publication. This feature required minimal additional effort, and we do not claim any novel technical contribution.
+
+We added a lightweight convolutional head and trained the normal output using a squared angular loss:
+
+$$
+\mathcal L_{\rm normal} = {1\over |\mathcal M|}\sum_{i\in\mathcal M} \angle (\hat{\mathbf n}_i,\mathbf n_i)^2
+$$
+
+where $\hat{\mathbf{n}}_i$ is the predicted normal, $\mathbf{n}_i$ is the ground-truth normal, and $\mathcal{M}$ denotes the set of valid pixels. For convenience, we did not collect ground-truth normal maps for training. Instead, we derived surface normals from the depth map and camera intrinsics. The resulting estimates are visually and numerically satisfactory.

docs/onnx.md

@@ -0,0 +1,89 @@
+# MoGe ONNX Support
+
+MoGe-2 is compatible with the ONNX format (opset version ≥ 14). We have exported several models for use in ONNXRuntime or deployment on other compatible inference engines.
+
+> **Important Note:** The `.infer()` method in our PyTorch code includes some post-processing logic (e.g., recovering focal and shift and reprojection) that cannot be exported to ONNX. The ONNX model only includes the raw forward() pass, which outputs intermediate predictions (affine point map, normal map, floating point mask, metric scale). You will need to implement any required post-processing steps separately if replicating the full inference pipeline.
+
+The exported models are in **FP32** precision, with **dynamic input resolution** and **variable-length** token support. You can further optimize these models based on your target deployment platform.
+
+<table>
+  <thead>
+    <tr>
+      <th>Version</th>
+      <th>Hugging Face Model</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+     <td rowspan="3">MoGe-2</td>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vitl-normal-onnx" target="_blank"><code>Ruicheng/moge-2-vitl-normal-onnx</code></a></td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vitb-normal-onnx" target="_blank"><code>Ruicheng/moge-2-vitb-normal-onnx</code></a></td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Ruicheng/moge-2-vits-normal-onnx" target="_blank"><code>Ruicheng/moge-2-vits-normal-onnx</code></a></td>
+    </tr>
+  </tbody>
+</table>
+
+## Customized Exportation
+
+### Dynamic Shape & Variable Number of Tokens
+```python
+import os
+os.environ['XFORMERS_DISABLED'] = '1'   # Disable xformers
+import numpy as np
+import torch
+from moge.model.v2 import MoGeModel
+
+PRETRAINED_MODEL = 'Ruicheng/moge-2-vits-normal.pt'
+ONNX_FILE = 'moge-2-vits-normal.onnx'
+
+model = MoGeModel.from_pretrained(PRETRAINED_MODEL)
+model.onnx_compatible_mode = True  # Enable ONNX compatible mode
+
+torch.onnx.export(
+    model, 
+    (torch.rand(1, 3, 518, 518), torch.tensor(1800)),
+    ONNX_FILE,
+    input_names=['image', 'num_tokens'],
+    output_names=['points', 'normal', 'mask', 'metric_scale'],
+    dynamic_axes={
+        'image': {0: 'batch_size', 2: 'height', 3: 'width'},
+    },
+    opset_version=14
+)
+```
+
+### Static Shape & Fixed Number of Tokens
+
+```python
+import os
+os.environ['XFORMERS_DISABLED'] = '1'   # Disable xformers
+import numpy as np
+import torch
+from moge.model.v2 import MoGeModel
+
+class MoGeStatic(MoGeModel):
+    def forward(self, image: torch.Tensor):
+        return super().forward(image, NUM_TOKENS)
+
+NUM_TOKENS = 1800
+FIXED_IMAGE_INPUT = torch.rand(1, 3, 518, 518)
+PRETRAINED_MODEL = 'Ruicheng/moge-2-vits-normal.pt'
+ONNX_FILE = 'moge-2-vits-normal.onnx'
+
+model = MoGeStatic.from_pretrained(PRETRAINED_MODEL)
+model.onnx_compatible_mode = True  # Enable ONNX compatible mode
+
+torch.onnx.export(
+    model, 
+    (FIXED_IMAGE_INPUT,),
+    ONNX_FILE,
+    input_names=['image'],
+    output_names=['points', 'normal', 'mask', 'metric_scale'],
+    dynamic_axes=None,
+    opset_version=14
+)
+```

docs/train.md

@@ -0,0 +1,181 @@
+
+# Training 
+
+This document provides instructions for training and finetuning the MoGe model.
+
+## Additional Requirements
+
+The following packages other than those listed in [`pyproject.toml`](../pyproject.toml) are required for training and finetuning the MoGe model:
+
+```
+accelerate
+sympy
+mlflow
+```
+
+## Data preparation
+
+### Dataset format
+
+Each dataset should be organized as follows:
+
+```
+somedataset
+├── .index.txt          # A list of instance paths
+├── folder1 
+│   ├── instance1       # Each instance is in a folder
+│   │   ├── image.jpg   # RGB image.
+│   │   ├── depth.png   # 16-bit depth. See moge/utils/io.py for details
+│   │   ├── meta.json   # Stores "intrinsics" as a 3x3 matrix
+│   │   └── ...         # Other componests such as segmentation mask, normal map etc.
+...
+```
+
+* `.index.txt` is placed at top directory to store a list of instance paths in this dataset. The dataloader will look for instances in this list. You may also use a custom split, e.g. `.train.txt`, `.val.txt` and specify it in the configuration file.
+
+* For depth images, it is recommended to use `read_depth()` and `write_depth()` in [`moge/utils/io.py`](../moge/utils/io.py) to read and write depth images. The depth is stored in logarithmic scale in 16-bit PNG format, offering a balanced precision, dynamic range and compression ratio compared to 16-bit and 32-bit EXR and linear depth formats. It also encodes `NaN` and `Inf` values for invalid depth values.
+
+* The `meta.json` should be a dictionary containing the key `intrinsics`, which are **normalized** camera parameters. You may put more metadata.
+
+* We also support reading and storing segementation masks for evaluation data (see paper evaluation of local points), which are saved in PNG format with semantic labels stored in png metadata as JSON strings. See `read_segmentation()` and `write_segmentation()` in [`moge/utils/io.py`](../moge/utils/io.py) for details.
+
+
+### Visual inspection
+
+We provide a script to visualize the data and check the data quality. It will export the instance as a PLY file for visualization of point cloud.
+
+```bash
+python moge/scripts/vis_data.py PATH_TO_INSTANCE --ply [-o SOMEWHERE_ELSE_TO_SAVE_VIS]
+```
+
+### DataLoader
+
+Our training dataloaders is customized to handle loading data, performing perspective crop, and augmentation in a multithreading pipeline. Please refer to [`moge/train/dataloader.py`](../moge/train/dataloader.py) if you have any concern.
+
+
+## Configuration
+
+See [`configs/train/v1.json`](../configs/train/v1.json) for an example configuration file. The configuration file defines the hyperparameters for training the MoGe model. 
+Here is a commented configuration for reference:
+
+```json
+{
+    "data": {
+        "aspect_ratio_range": [0.5, 2.0],               # Range of aspect ratio of sampled images
+        "area_range": [250000, 1000000],                # Range of sampled image area in pixels
+        "clamp_max_depth": 1000.0,                      # Maximum far/near
+        "center_augmentation": 0.5,                     # Ratio of center crop augmentation
+        "fov_range_absolute": [1, 179],                 # Absolute range of FOV in degrees
+        "fov_range_relative": [0.01, 1.0],              # Relative range of FOV to the original FOV
+        "image_augmentation": ["jittering", "jpeg_loss", "blurring"],       # List of image augmentation techniques
+        "datasets": [ 
+            {
+                "name": "TartanAir",                    # Name of the dataset. Name it as you like.
+                "path": "data/TartanAir",               # Path to the dataset
+                "label_type": "synthetic",              # Label type for this dataset. Losses will be applied accordingly. see "loss" config
+                "weight": 4.8,                          # Probability of sampling this dataset
+                "index": ".index.txt",                  # File name of the index file.  Defaults to .index.txt
+                "depth": "depth.png",                   # File name of depth images. Defaults to depth.png
+                "center_augmentation": 0.25,            # Below are dataset-specific hyperparameters. Overriding the global ones above.
+                "fov_range_absolute": [30, 150],
+                "fov_range_relative": [0.5, 1.0],
+                "image_augmentation": ["jittering", "jpeg_loss", "blurring", "shot_noise"]
+            }
+        ]
+    },
+    "model_version": "v1",                 # Model version. If you have multiple model variants, you can use this to switch between them.
+    "model": {                             # Model hyperparameters. Will be passed to Model __init__() as kwargs.
+        "encoder": "dinov2_vitl14",
+        "remap_output": "exp",
+        "intermediate_layers": 4,
+        "dim_upsample": [256, 128, 64],
+        "dim_times_res_block_hidden": 2,
+        "num_res_blocks": 2,
+        "num_tokens_range": [1200, 2500],
+        "last_conv_channels": 32,
+        "last_conv_size": 1
+    },
+    "optimizer": {                          # Reflection-like optimizer configurations. See moge.train.utils.py build_optimizer() for details.
+        "type": "AdamW",
+        "params": [
+            {"params": {"include": ["*"], "exclude": ["*backbone.*"]}, "lr": 1e-4},
+            {"params": {"include": ["*backbone.*"]}, "lr": 1e-5}
+        ]
+    },
+    "lr_scheduler": {                       # Reflection-like lr_scheduler configurations. See moge.train.utils.py build_lr_scheduler() for details.
+        "type": "SequentialLR",
+        "params": {
+            "schedulers": [
+                {"type": "LambdaLR", "params": {"lr_lambda": ["1.0", "max(0.0, min(1.0, (epoch - 1000) / 1000))"]}},
+                {"type": "StepLR", "params": {"step_size": 25000, "gamma": 0.5}}
+            ],
+            "milestones": [2000]
+        }
+    },
+    "low_resolution_training_steps": 50000, # Total number of low-resolution training steps. It makes the early stage training faster. Later stage training on varying size images will be slower.
+    "loss": {
+        "invalid": {},                      # invalid instance due to runtime error when loading data
+        "synthetic": {                      # Below are loss hyperparameters
+            "global": {"function": "affine_invariant_global_loss", "weight": 1.0, "params": {"align_resolution": 32}},
+            "patch_4": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 4, "align_resolution": 16, "num_patches": 16}},
+            "patch_16": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 16, "align_resolution": 8, "num_patches": 256}},
+            "patch_64": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 64, "align_resolution": 4, "num_patches": 4096}},
+            "normal": {"function": "normal_loss", "weight": 1.0},
+            "mask": {"function": "mask_l2_loss", "weight": 1.0}
+        },
+        "sfm": {
+            "global": {"function": "affine_invariant_global_loss", "weight": 1.0, "params": {"align_resolution": 32}},
+            "patch_4": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 4, "align_resolution": 16, "num_patches": 16}},
+            "patch_16": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 16, "align_resolution": 8, "num_patches": 256}},
+            "mask": {"function": "mask_l2_loss", "weight": 1.0}
+        },
+        "lidar": {
+            "global": {"function": "affine_invariant_global_loss", "weight": 1.0, "params": {"align_resolution": 32}},
+            "patch_4": {"function": "affine_invariant_local_loss", "weight": 1.0, "params": {"level": 4, "align_resolution": 16, "num_patches": 16}},
+            "mask": {"function": "mask_l2_loss", "weight": 1.0}
+        }
+    }
+}
+```
+
+## Run Training 
+
+Launch the training script [`moge/scripts/train.py`](../moge/scripts/train.py). Note that we use [`accelerate`](https://github.com/huggingface/accelerate) for distributed training. 
+
+```bash
+accelerate launch \
+    --num_processes 8 \
+    moge/scripts/train.py \
+    --config configs/train/v1.json \
+    --workspace workspace/debug \
+    --gradient_accumulation_steps 2 \
+    --batch_size_forward 2 \
+    --checkpoint latest \
+    --enable_gradient_checkpointing True \
+    --vis_every 1000 \
+    --enable_mlflow True
+```
+
+
+## Finetuning
+
+To finetune the pre-trained MoGe model, download the model checkpoint and put it in a local directory, e.g. `pretrained/moge-vitl.pt`.
+
+> NOTE: when finetuning pretrained MoGe model, a much lower learning rate is required. 
+The suggested learning rate for finetuning is not greater than 1e-5 for the head and 1e-6 for the backbone. 
+And the batch size is recommended to be 32 at least. 
+The settings in default configuration are not optimal for specific datasets and may require further tuning.
+
+```bash
+accelerate launch \
+    --num_processes 8 \
+    moge/scripts/train.py \
+    --config configs/train/v1.json \
+    --workspace workspace/debug \
+    --gradient_accumulation_steps 2 \
+    --batch_size_forward 2 \
+    --checkpoint pretrained/moge-vitl.pt \
+    --enable_gradient_checkpointing True \
+    --vis_every 1000 \
+    --enable_mlflow True
+```

eval_output/_eval_all_20260514_010406.summary.txt

@@ -0,0 +1,2 @@
+[OK] da3_mono -> eval_output/da3_mono_20260514_010406.json (5861 bytes) at Thu May 14 01:23:28 AM AEST 2026
+[OK] depth_pro -> eval_output/depth_pro_20260514_010406.json (8029 bytes) at Thu May 14 02:25:48 AM AEST 2026

eval_output/_eval_all_20260514_051015.summary.txt

@@ -0,0 +1,5 @@
+[OK] marigold -> eval_output/marigold_20260514_051015.json (3009 bytes) at Thu May 14 05:55:14 AM AEST 2026
+[OK] lotus -> eval_output/lotus_20260514_051015.json (3056 bytes) at Thu May 14 06:17:39 AM AEST 2026
+[OK] depthmaster -> eval_output/depthmaster_20260514_051015.json (3015 bytes) at Thu May 14 06:50:08 AM AEST 2026
+[OK] ppd -> eval_output/ppd_20260514_051015.json (3008 bytes) at Thu May 14 07:46:42 AM AEST 2026
+[OK] fe2e -> eval_output/fe2e_20260514_051015.json (3005 bytes) at Thu May 14 09:50:45 AM AEST 2026

eval_output/da2_dpt_vitb_20260114_134612.json

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+}

eval_output/da2_public_vitl_subset_20260512_180834.json

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+}

eval_output/da2_sdt_vitb_20260114_161729.json

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+}

eval_output/da3_dpt_20260114_145611.json

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+}

eval_output/da3_dualdpt_20260114_145615.json

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eval_output/da3_mono_20260514_010406.json

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eval_output/da3_sdt_20260114_151926.json

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eval_output/depth_pro_20260514_010406.json

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eval_output/depthmaster_20260514_051015.json

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eval_output/fe2e_20260514_051015.json

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eval_output/lotus_20260514_051015.json

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eval_output/lotus_v1_20260514_120539.json

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eval_output/marigold_20260514_051015.json

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eval_output/ppd_20260514_051015.json

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+}

eval_output/vggt_dpt_20260114_154929.json

@@ -0,0 +1,104 @@
+{
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+}

eval_output/vggt_dpt_metric_20260115_225801.json

@@ -0,0 +1,169 @@
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+}

eval_output/vggt_sdt_20260114_154947.json

@@ -0,0 +1,104 @@
+{
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+}

eval_output/vggt_sdt_metric_20260115_235001.json

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+{
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+}

eval_scripts/eval_all_slurm.sh

@@ -0,0 +1,149 @@
+#!/bin/bash
+#SBATCH --job-name=eval-all
+#SBATCH --output=/home/ywan0794/MoGe/eval_all_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/eval_all_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-12:00:00
+#SBATCH --mem=80G
+#SBATCH --nodelist=erinyes
+# Single sbatch — production run for 7 models on all 10 MoGe benchmarks, serial,
+# one H100 held the whole time. Failures don't abort; we log & continue.
+# Model order: cheap → expensive (FE2E last so it doesn't block others if it crashes).
+
+export PYTHONUNBUFFERED=1
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+CONFIG_FE2E=/home/ywan0794/MoGe/configs/eval/fe2e_all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+SUMMARY=$OUT_DIR/_eval_all_${TIMESTAMP}.summary.txt
+: > $SUMMARY
+
+echo "============================================"
+echo "eval-all started at $(date)"
+echo "Config (main): $CONFIG"
+echo "Config (fe2e): $CONFIG_FE2E"
+echo "TIMESTAMP: $TIMESTAMP"
+echo "Summary file: $SUMMARY"
+echo "============================================"
+nvidia-smi
+
+run_model() {
+  # Usage: run_model <label> <env> <config> <python invocation ...>
+  local label=$1 env=$2 cfg=$3
+  shift 3
+  echo
+  echo "============================================"
+  echo "[$label] starting at $(date)  (conda env: $env)"
+  echo "============================================"
+  conda deactivate 2>/dev/null || true
+  conda activate $env
+  echo "Active env: $CONDA_DEFAULT_ENV"
+  export PYTHONPATH=$PYTHONPATH:$(pwd)
+  python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+  local OUTFILE=$OUT_DIR/${label}_${TIMESTAMP}.json
+
+  if "$@" \
+        --baseline baselines/${label}.py \
+        --config $cfg \
+        --output $OUTFILE; then
+    if [ -f $OUTFILE ]; then
+      local SIZE=$(stat -c%s $OUTFILE 2>/dev/null)
+      echo "[OK] $label -> $OUTFILE (${SIZE} bytes) at $(date)" | tee -a $SUMMARY
+    else
+      echo "[NO-OUTPUT] $label (exited 0 but no JSON) at $(date)" | tee -a $SUMMARY
+    fi
+  else
+    rc=$?
+    echo "[FAIL rc=$rc] $label at $(date)" | tee -a $SUMMARY
+  fi
+}
+
+# ============================================
+# 1) DA3-Mono — SKIPPED, already done in eval_output/da3_mono_20260514_010406.json
+# ============================================
+# REPO=/home/ywan0794/EvalMDE/Depth-Anything-3
+# HF_ID=depth-anything/DA3MONO-LARGE
+# run_model da3_mono da3 $CONFIG \
+#   python moge/scripts/eval_baseline.py \
+#         --repo $REPO --hf_id $HF_ID
+
+# ============================================
+# 2) Depth Pro — SKIPPED, already done in eval_output/depth_pro_20260514_010406.json
+# ============================================
+# REPO=/home/ywan0794/EvalMDE/ml-depth-pro
+# CKPT=$REPO/checkpoints/depth_pro.pt
+# run_model depth_pro depth-pro $CONFIG \
+#   python moge/scripts/eval_baseline.py \
+#         --repo $REPO --checkpoint $CKPT --precision fp32
+
+# ============================================
+# 3) Marigold v1.1 (env: marigold) — paper-canonical via
+#    `script/depth/eval/11_infer_nyu.sh`: v1-1 + denoise=1 + ensemble=10 + seed=1234.
+#    v1-1 retrained to match v1-0's denoise=50 quality at denoise=1.
+# ============================================
+REPO=/home/ywan0794/EvalMDE/Marigold
+CHECKPOINT=prs-eth/marigold-depth-v1-1
+run_model marigold marigold $CONFIG \
+  python moge/scripts/eval_baseline.py \
+        --repo $REPO --checkpoint $CHECKPOINT \
+        --denoise_steps 4 --ensemble_size 1
+
+# ============================================
+# 4) Lotus (env: lotus) — paper-canonical eval.sh:
+#    generative v2-1-disparity + half_precision + seed=42.
+# ============================================
+REPO=/home/ywan0794/EvalMDE/Lotus
+PRETRAINED=jingheya/lotus-depth-g-v2-1-disparity
+run_model lotus lotus $CONFIG \
+  python moge/scripts/eval_baseline.py \
+        --repo $REPO --pretrained $PRETRAINED --mode generation \
+        --task_name depth --disparity --timestep 999 --fp16 --seed 42
+
+# ============================================
+# 5) DepthMaster (env: depthmaster)
+# ============================================
+REPO=/home/ywan0794/EvalMDE/DepthMaster
+CKPT=$REPO/ckpt/eval
+run_model depthmaster depthmaster $CONFIG \
+  python moge/scripts/eval_baseline.py \
+        --repo $REPO --checkpoint $CKPT --processing_res 768
+
+# ============================================
+# 6) PPD (env: ppd) — needs DA2 vitl semantics
+# ============================================
+REPO=/home/ywan0794/EvalMDE/Pixel-Perfect-Depth
+# Paper-canonical eval.yaml: semantics=MoGe2, ppd_moge.pth, sampling_steps=4
+run_model ppd ppd $CONFIG \
+  python moge/scripts/eval_baseline.py \
+        --repo $REPO --semantics_model MoGe2 \
+        --semantics_pth checkpoints/moge2.pt \
+        --model_pth checkpoints/ppd_moge.pth --sampling_steps 4
+
+# ============================================
+# 7) FE2E (env: fe2e) — slowest, last
+# ============================================
+REPO=/home/ywan0794/EvalMDE/FE2E
+MODEL_PATH=$REPO/pretrain
+LORA_PATH=$REPO/lora/LDRN.safetensors
+run_model fe2e fe2e $CONFIG_FE2E \
+  python moge/scripts/eval_baseline.py \
+        --repo $REPO --model_path $MODEL_PATH --lora_path $LORA_PATH \
+        --prompt_type empty --single_denoise --cfg_guidance 6.0 --size_level 768
+
+# ============================================
+echo
+echo "============================================"
+echo "eval-all finished at $(date)"
+echo "============================================"
+echo "=== Summary ==="
+cat $SUMMARY

eval_scripts/eval_da2_dpt_slurm.sh

@@ -0,0 +1,85 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da2-dpt
+#SBATCH --output=moge_da2_dpt_%j.log
+#SBATCH --error=moge_da2_dpt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da2
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+ENCODER=vitb
+DECODER=dpt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/Depth-Anything-V2/training/exp/dpt_vitb_both/epoch_007.pth"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA2+DPT at $(date)"
+echo "Encoder: $ENCODER"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/da2_dpt_${ENCODER}.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da2_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da2_dpt_${ENCODER}_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-V2 \
+    --checkpoint "$CKPT" \
+    --encoder $ENCODER \
+    --decoder $DECODER
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da2_sdt_slurm.sh

@@ -0,0 +1,85 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da2-sdt
+#SBATCH --output=moge_da2_sdt_%j.log
+#SBATCH --error=moge_da2_sdt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da2
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+ENCODER=vitb
+DECODER=sdt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/Depth-Anything-V2/training/exp/sdt_vitb_both/epoch_008.pth"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA2+SDT at $(date)"
+echo "Encoder: $ENCODER"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/da2_sdt_${ENCODER}.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da2_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da2_sdt_${ENCODER}_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-V2 \
+    --checkpoint "$CKPT" \
+    --encoder $ENCODER \
+    --decoder $DECODER
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da2_slurm.sh

@@ -0,0 +1,77 @@
+#!/bin/bash
+#SBATCH --job-name=moge-eval-da2
+#SBATCH --output=moge_eval_da2_%j.log
+#SBATCH --error=moge_eval_da2_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da2
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+BACKBONE=vitl  # vits, vitb, vitl
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA2 at $(date)"
+echo "Backbone: $BACKBONE"
+echo "Output: ${OUT_DIR}/da2_${BACKBONE}.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da_v2.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da2_${BACKBONE}_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-V2 \
+    --backbone $BACKBONE
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da3_dpt_slurm.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da3-dpt
+#SBATCH --output=moge_da3_dpt_%j.log
+#SBATCH --error=moge_da3_dpt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=60G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da3
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+DECODER=dpt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/Depth-Anything-3/training/exp/da3_dpt_vitl_both/epoch_010.pth"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA3+DPT at $(date)"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/da3_dpt.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da3_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da3_dpt_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-3 \
+    --checkpoint "$CKPT" \
+    --decoder $DECODER
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da3_dualdpt_slurm.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da3-dualdpt
+#SBATCH --output=moge_da3_dualdpt_%j.log
+#SBATCH --error=moge_da3_dualdpt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=60G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da3
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+DECODER=dualdpt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/Depth-Anything-3/training/exp/da3_dualdpt_vitl_both/epoch_010.pth"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA3+DualDPT at $(date)"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/da3_dualdpt.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da3_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da3_dualdpt_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-3 \
+    --checkpoint "$CKPT" \
+    --decoder $DECODER
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da3_mono_slurm.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da3-mono
+#SBATCH --output=/home/ywan0794/MoGe/moge_da3_mono_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_da3_mono_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-04:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate da3
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/Depth-Anything-3
+HF_ID=depth-anything/DA3MONO-LARGE
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for DA3-Mono at $(date)"
+echo "Repo: $REPO"
+echo "HF id: $HF_ID"
+echo "Config: $CONFIG"
+echo "============================================"
+
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da3_mono.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/da3_mono_${TIMESTAMP}.json \
+    --repo $REPO \
+    --hf_id $HF_ID
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da3_sdt_slurm.sh

@@ -0,0 +1,82 @@
+#!/bin/bash
+#SBATCH --job-name=moge-da3-sdt
+#SBATCH --output=moge_da3_sdt_%j.log
+#SBATCH --error=moge_da3_sdt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=60G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da3
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+DECODER=sdt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/Depth-Anything-3/training/exp/da3_sdt_vitl_both/epoch_010.pth"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA3+SDT at $(date)"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/da3_sdt.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da3_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/da3_sdt_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-3 \
+    --checkpoint "$CKPT" \
+    --decoder $DECODER
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_da3_slurm.sh

@@ -0,0 +1,77 @@
+#!/bin/bash
+#SBATCH --job-name=moge-eval-da3
+#SBATCH --output=moge_eval_da3_%j.log
+#SBATCH --error=moge_eval_da3_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=60G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate da3
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+MODEL_NAME=da3-large  # da3-base, da3-large, da3-giant
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for DA3 at $(date)"
+echo "Model: $MODEL_NAME"
+echo "Output: ${OUT_DIR}/da3_${MODEL_NAME}.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/da3.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/${MODEL_NAME}_${TIMESTAMP}.json \
+    --repo /home/ywan0794/Depth-Anything-3 \
+    --model_name $MODEL_NAME
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_depth_pro_slurm.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+#SBATCH --job-name=moge-depth-pro
+#SBATCH --output=/home/ywan0794/MoGe/moge_depth_pro_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_depth_pro_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-04:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate depth-pro
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.9/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/ml-depth-pro
+CKPT=$REPO/checkpoints/depth_pro.pt
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for Depth Pro at $(date)"
+echo "Repo: $REPO"
+echo "Checkpoint: $CKPT"
+echo "Config: $CONFIG"
+echo "============================================"
+
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/depth_pro.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/depth_pro_${TIMESTAMP}.json \
+    --repo $REPO \
+    --checkpoint $CKPT \
+    --precision fp32
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_depthmaster_slurm.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+#SBATCH --job-name=moge-depthmaster
+#SBATCH --output=/home/ywan0794/MoGe/moge_depthmaster_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_depthmaster_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-04:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate depthmaster
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/DepthMaster
+# README: download HF zysong212/DepthMaster into ckpt/eval (relative to repo)
+CKPT=$REPO/ckpt/eval
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for DepthMaster at $(date)"
+echo "Repo: $REPO"
+echo "Checkpoint dir: $CKPT"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# DepthMaster infer.sh default: --processing_res 768
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/depthmaster.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/depthmaster_${TIMESTAMP}.json \
+    --repo $REPO \
+    --checkpoint $CKPT \
+    --processing_res 768
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_fe2e_slurm.sh

@@ -0,0 +1,70 @@
+#!/bin/bash
+#SBATCH --job-name=moge-fe2e
+#SBATCH --output=/home/ywan0794/MoGe/moge_fe2e_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_fe2e_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=80G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate fe2e
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+# FE2E pins torch 2.6 / diffusers 0.32.2 fork in its own env.
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/FE2E
+MODEL_PATH=$REPO/pretrain
+LORA_PATH=$REPO/lora/LDRN.safetensors
+CONFIG=/home/ywan0794/MoGe/configs/eval/fe2e_all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for FE2E at $(date)"
+echo "Repo: $REPO"
+echo "model_path: $MODEL_PATH"
+echo "lora_path: $LORA_PATH"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# Mirror README depth-eval call:
+#   --single_denoise --prompt_type empty --task_name depth --cfg_guidance 6.0
+# Default size_level matches README 768.
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/fe2e.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/fe2e_${TIMESTAMP}.json \
+    --repo $REPO \
+    --model_path $MODEL_PATH \
+    --lora_path $LORA_PATH \
+    --prompt_type empty \
+    --single_denoise \
+    --cfg_guidance 6.0 \
+    --size_level 768
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_lotus_slurm.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+#SBATCH --job-name=moge-lotus
+#SBATCH --output=/home/ywan0794/MoGe/moge_lotus_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_lotus_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-08:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate lotus
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/Lotus
+PRETRAINED=jingheya/lotus-depth-d-v2-0-disparity
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for Lotus at $(date)"
+echo "Repo: $REPO"
+echo "Checkpoint: $PRETRAINED"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# Lotus disparity v2 regression: --disparity flag tells the wrapper to emit
+# `disparity_affine_invariant`. For depth ckpts (e.g. lotus-depth-d-v1-0), drop --disparity.
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/lotus.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/lotus_${TIMESTAMP}.json \
+    --repo $REPO \
+    --pretrained $PRETRAINED \
+    --mode regression \
+    --task_name depth \
+    --disparity \
+    --timestep 999
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_lotus_v1_slurm.sh

@@ -0,0 +1,69 @@
+#!/bin/bash
+#SBATCH --job-name=moge-lotus-v1
+#SBATCH --output=/home/ywan0794/MoGe/moge_lotus_v1_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_lotus_v1_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-04:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+# MoGe protocol full eval on 10 benchmarks with Lotus depth ckpt v1-0 (depth output).
+# Chosen over v2-1-disparity for uniform `depth_affine_invariant` output across 7 models.
+# v1-0 is the original Lotus depth ckpt (Lotus paper, 2024-09); v2-1-disparity (2024-11)
+# achieves better numbers per README, but emits disparity_affine_invariant — not directly
+# comparable in depth space to the other 6 models.
+
+export PYTHONUNBUFFERED=1
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate lotus
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=${PYTHONPATH:-}:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "Ckpt: jingheya/lotus-depth-g-v1-0 (depth output, generation mode)"
+echo "============================================"
+
+nvidia-smi
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/Lotus
+PRETRAINED=jingheya/lotus-depth-g-v1-0
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for Lotus v1-0 (depth ckpt) at $(date)"
+echo "Repo: $REPO"
+echo "Checkpoint: $PRETRAINED"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# Paper-canonical from Lotus eval.sh: generation mode, fp16, seed=42, timestep=999.
+# NO --disparity flag (v1-0 outputs depth, not disparity).
+# Wrapper auto-emits `depth_affine_invariant` when --disparity is absent.
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/lotus.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/lotus_v1_${TIMESTAMP}.json \
+    --repo $REPO \
+    --pretrained $PRETRAINED \
+    --mode generation \
+    --task_name depth \
+    --timestep 999 \
+    --fp16 \
+    --seed 42
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_marigold_slurm.sh

@@ -0,0 +1,63 @@
+#!/bin/bash
+#SBATCH --job-name=moge-marigold
+#SBATCH --output=/home/ywan0794/MoGe/moge_marigold_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_marigold_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-08:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate marigold
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/Marigold
+CHECKPOINT=prs-eth/marigold-depth-v1-1
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for Marigold v1.1 at $(date)"
+echo "Repo: $REPO"
+echo "Checkpoint: $CHECKPOINT"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# Marigold defaults from official run.py: ensemble_size=1, denoise_steps=None (use checkpoint default),
+# processing_res=None (use checkpoint default), fp16 ON for speed/VRAM (per run.py example).
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/marigold.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/marigold_${TIMESTAMP}.json \
+    --repo $REPO \
+    --checkpoint $CHECKPOINT \
+    --ensemble_size 1 \
+    --fp16
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_ppd_slurm.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+#SBATCH --job-name=moge-ppd
+#SBATCH --output=/home/ywan0794/MoGe/moge_ppd_%j.log
+#SBATCH --error=/home/ywan0794/MoGe/moge_ppd_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:H100:1
+#SBATCH --time=0-08:00:00
+#SBATCH --mem=40G
+#SBATCH --nodelist=erinyes
+
+export PYTHONUNBUFFERED=1
+
+cd /home/ywan0794/MoGe
+
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+conda activate ppd
+
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+echo "=== GPU Info ==="
+nvidia-smi
+
+python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"
+
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+REPO=/home/ywan0794/EvalMDE/Pixel-Perfect-Depth
+CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
+OUT_DIR=eval_output
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Eval for Pixel-Perfect-Depth at $(date)"
+echo "Repo: $REPO"
+echo "Config: $CONFIG"
+echo "============================================"
+
+# PPD README: ppd.pth + depth_anything_v2_vitl.pth under <repo>/checkpoints/.
+# sampling_steps=4 is run.py default.
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/ppd.py \
+    --config $CONFIG \
+    --output ${OUT_DIR}/ppd_${TIMESTAMP}.json \
+    --repo $REPO \
+    --semantics_model DA2 \
+    --semantics_pth checkpoints/depth_anything_v2_vitl.pth \
+    --model_pth checkpoints/ppd.pth \
+    --sampling_steps 4
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_vggt_dpt_metric_slurm.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+#SBATCH --job-name=vggt-dpt-metric
+#SBATCH --output=vggt_dpt_metric_%j.log
+#SBATCH --error=vggt_dpt_metric_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=80G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate vggt
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+DECODER=dpt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/vggt/training/logs/dpt_metric_lora/ckpts/checkpoint_4.pt"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for VGGT+DPT (Metric) at $(date)"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/vggt_dpt_metric_${TIMESTAMP}.json"
+echo "============================================"
+
+# ============================================
+# 运行评估 (使用 vggt_metric.py baseline 和 metric_benchmarks.json)
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/vggt_metric.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/metric_benchmarks.json \
+    --output ${OUT_DIR}/vggt_dpt_metric_${TIMESTAMP}.json \
+    --repo /home/ywan0794/vggt \
+    --checkpoint "$CKPT" \
+    --decoder $DECODER \
+    --lora_rank 8 \
+    --lora_alpha 16
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"

eval_scripts/eval_vggt_dpt_slurm.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+#SBATCH --job-name=moge-vggt-dpt
+#SBATCH --output=moge_vggt_dpt_%j.log
+#SBATCH --error=moge_vggt_dpt_%j.log
+#SBATCH --open-mode=append
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:1
+#SBATCH --time=1-00:00:00
+#SBATCH --mem=80G
+#SBATCH --nodelist=hades
+
+# 禁用Python输出缓冲
+export PYTHONUNBUFFERED=1
+
+# 进入MoGe目录
+cd /home/ywan0794/MoGe
+
+# 初始化并激活conda环境
+source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
+
+# 激活环境
+conda activate vggt
+
+# 设置 CUDA 环境变量
+export CUDA_HOME=$CONDA_PREFIX
+export PATH=$CUDA_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
+
+# 设置Python路径
+export PYTHONPATH=$PYTHONPATH:$(pwd)
+
+# 确认环境
+echo "============================================"
+echo "Activated conda environment: $CONDA_DEFAULT_ENV"
+echo "CUDA_HOME: $CUDA_HOME"
+echo "============================================"
+
+# 显示GPU信息
+echo "=== GPU Info ==="
+nvidia-smi
+
+# 检查CUDA是否可用
+python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU count:', torch.cuda.device_count()); print('GPU name:', torch.cuda.get_device_name(0))"
+
+# ============================================
+# 评估配置
+# ============================================
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+
+# 模型配置
+DECODER=dpt
+
+# Checkpoint路径
+CKPT="/home/ywan0794/vggt/training/logs/dpt_metric_lora/ckpts/checkpoint_4.pt"
+
+# 输出目录
+OUT_DIR="eval_output"
+mkdir -p $OUT_DIR
+
+echo "============================================"
+echo "Starting MoGe Evaluation for VGGT+DPT at $(date)"
+echo "Decoder: $DECODER"
+echo "Checkpoint: $CKPT"
+echo "Output: ${OUT_DIR}/vggt_dpt.json"
+echo "============================================"
+
+# ============================================
+# 运行评估
+# ============================================
+python moge/scripts/eval_baseline.py \
+    --baseline baselines/vggt_custom.py \
+    --config /home/ywan0794/datasets/eval/moge_style_eval/all_benchmarks.json \
+    --output ${OUT_DIR}/vggt_dpt_${TIMESTAMP}.json \
+    --repo /home/ywan0794/vggt \
+    --checkpoint "$CKPT" \
+    --decoder $DECODER \
+    --lora_rank 8 \
+    --lora_alpha 16
+
+echo "============================================"
+echo "Evaluation completed at $(date)"
+echo "============================================"