PHASE0_RESULTS.md

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.

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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).

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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	—	—	—

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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

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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

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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

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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

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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

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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°

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⚠️ 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.

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🆕 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.

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🆕 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.

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🎯 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:

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🥇 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

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🥈 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.

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🥉 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

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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.