README.md

---
license: other
license_name: cm-evs-mixed
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- depth-estimation
- image-to-image
tags:
- panoramic
- equirectangular
- erp
- rgb-d
- viewpoint-selection
- view-planning
- data-curation
- 3d-scene
- indoor-scene-understanding
- world-model
- novel-view-synthesis
pretty_name: CM-EVS — Coverage-Curated Panoramic RGB-D Dataset
---

# CM-EVS: A Coverage-Curated Panoramic RGB-D Dataset for Indoor Scene Understanding

CM-EVS is a curated panoramic RGB-D dataset built under a single principle: **maximize the geometric coverage of a 3D scene with the fewest equirectangular (ERP) frames possible**. The release is structured as one redistributable Blender indoor data archive plus four license-aware adapter packages that regenerate matched frames locally from upstream sources whose terms forbid redistribution.

> **v1.0 status**: this version stages the **full Blender indoor data drop** (374 scene instances, 13,631 ERP RGB-depth-pose frames; 201 from the round1+2 sampling and 173 from round2). The paper's headline `326 scenes / 11,583 frames` is the curator-selected subset that will be derived from this drop after the §5 evaluation experiments finalize. See `TODO.md` for items still in flight.

## Dataset summary

| Source | License | Released here | Scenes / frames |
| --- | --- | --- | --- |
| **Blender indoor** | CC-BY 4.0 | **Full data** (`blender_indoor/`) | 374 / 13,631 |
| HM3D | upstream EULA | Adapter only (`adapters/hm3d/`) | 401 rooms / regen-only |
| ScanNet++ | upstream ToS | Adapter only (`adapters/scannetpp/`) | 500 scans / regen-only |
| OB3D (outdoor) | upstream license | Adapter only (`adapters/ob3d/`) | 24 / regen-only |
| TartanGround (outdoor) | upstream license | Adapter only (`adapters/tartanground/`) | 762 parts / regen-only |

The Blender indoor frames are the only redistributable RGB-D data. For the four restricted sources, this dataset ships the per-source adapter (config + pipeline script + scene-id metadata); users obtain upstream data themselves and run the adapter locally to reproduce matching ERP frames under the unified schema.

## Output schema

Every released ERP frame follows a single coordinate convention:

- World frame: right-handed, `+X` right, `+Y` up, `+Z` forward
- Camera frame: OpenCV (`+x` image right, `+y` image down, `+z` camera forward)
- Pose: scalar-first quaternion `q_wc = [w, x, y, z]` plus position `C_w − C_{w,0}` relative to the scene's first selected frame (the absolute first-frame center is recorded once per scene in `meta.json` when present)
- ERP pixel coords: longitude `(u/W − 0.5) · 2π`, latitude `(0.5 − v/H) · π`
- Range depth: each pixel stores radial distance from camera center to surface (not perspective `z`-depth). NaN or 0 marks invalid pixels.

| File | Format | Description |
| --- | --- | --- |
| `panorama_{NNNN}.png` | PNG, 2048×1024 | ERP RGB image |
| `panorama_{NNNN}_depth.npy` | float32 array | ERP range depth (m); NaN or 0 if invalid; absent for some frames where depth was not produced |
| `pose_{NNNN}.json` | JSON | `q_wc`, position, `camera_type` |

Per-scene `meta.json`, `metadata/selected_viewpoints.json`, `metadata/candidates.jsonl`, `metadata/per_step_log.jsonl` (curator-only) will land here once the curator runs on the merged 374-scene set; see `TODO.md`.

## Directory layout

```
cmevs_hf_release/
├── README.md            (this file — HF dataset card)
├── LICENSE.md           (mixed-license matrix)
├── CHANGELOG.md
├── croissant.json       (MLCommons Croissant v1.0; passes mlcroissant 1.1 validator)
├── SHA256SUMS           (top-level checksums, excluding blender_indoor/scenes/)
├── TODO.md              (pre-push checklist)
├── blender_indoor/
│   ├── README.md
│   ├── scenes/sence_indoor_{0001..0374}/{panorama,pose}_{NNNN}.{png,npy,json}
│   ├── SHA256SUMS       (39,896 lines for 13,631 frames × ~3 files)
│   └── metadata/{source_manifest.json, splits.json, frame_manifest.csv,
│                  scene_id_mapping.csv, frame_id_mapping.csv}
├── adapters/{hm3d, scannetpp, ob3d, tartanground}/
│   ├── README.md
│   ├── config.yaml
│   ├── pipeline.py / reencoding_script.md
│   └── metadata/source_manifest.json
├── code/                (curator core modules + scripts; reviewer reference)
└── results/             (paper §5 result CSVs; placeholders to be filled)
```

## Datasheet

Following Gebru et al. 2021. (Source: `main.tex` Appendix A — content here is a faithful markdown rendering; cross-check against the paper PDF.)

### Motivation

**Purpose.** Evaluates fixed-budget panoramic viewpoint curation policies on existing 3D assets, and provides reproducible ERP RGB-D-pose samples for panoramic perception experiments.

**Creators / funding.** Anonymized during double-blind review; finalized in camera-ready.

### Composition

**Instances.** Each instance is an ERP frame triple (RGB image + range-depth array + camera pose), plus per-scene `meta.json` and curator-only provenance metadata.

**Counts.** v1.0 stages 13,631 ERP frames across 374 Blender indoor scene instances (CC-BY 4.0). The four restricted sources (HM3D / ScanNet++ / OB3D / TartanGround) ship adapters only; users regenerate matching frames locally.

**Sampling.** Indoor (Blender) frames are produced offline by Cycles ERP rendering. The 374 scene instances comprise 201 from round1+2 (Blender_indoor_FOU_threshold-0.2, rounds 1+2 merge) and 173 from round2 (independent extraction). 48 original `sence_indoor_XXXX` ids appear in both rounds with different sampling outcomes; both versions are kept and renumbered (see `blender_indoor/metadata/scene_id_mapping.csv` for traceability). Outdoor source trajectories (TartanGround, OB3D) are re-encoded into the unified schema by the `adapters/{tartanground,ob3d}/` pipelines; the curator does not run on outdoor sources in v1.0.

**Fields.** RGB PNG (2048×1024 for Blender indoor; native source resolution otherwise), float32 range depth (`.npy`), pose JSON with scalar-first `q_wc`, `meta.json`, candidate / viewpoint / per-step-log metadata (curator-produced frames only), source / scene / split ids.

**Missing values.** Invalid depth pixels are NaN or 0 by source convention; per-frame invalid-depth ratio statistics will land in `results/frame_quality.csv` (see TODO).

**Splits.** Default scene-level 70 / 15 / 15 split via `sha256(new_scene_id) % 100`. See `blender_indoor/metadata/splits.json`. The downstream panoramic-depth experiment (paper §4.10) uses a separate 94-scene Blender-indoor subset under its own scene-level split (84 / 10 / 10 = 3,400 / 362 / 423 frames).

### Collection

Indoor data is produced by the CM-EVS pipeline (asset loading, coordinate normalization, candidate generation, 26-direction geometric-validity filtering, conflict-aware greedy selection, 2048×1024 high-resolution Cycles ERP rendering, export under the unified schema). Outdoor data (TartanGround, OB3D) is re-encoded into the unified schema; the curator does **not** run on outdoor sources in v1.0. HM3D and ScanNet++ frames are not redistributed: the release ships adapter regeneration scripts that produce matched frames locally after the user accepts upstream license terms. No new human-subject data is collected.

### Preprocessing

Coordinate normalization to right-handed `+X`-right `+Y`-up `+Z`-forward world frame with the OpenCV-style camera frame; pose stored as scalar-first `q_wc = [w, x, y, z]` plus position relative to the scene's first selected frame. AABB computation; source-specific candidate generation; 26-direction geometric-validity filter. Cubemap-to-ERP re-encoding at native resolution for outdoor sources; optional exposure adjustment for Blender; output schema conversion. Raw upstream 3D assets are not redistributed unless upstream licenses allow.

### Uses

**Recommended:** panoramic depth estimation, ERP novel-view synthesis, data-centric viewpoint policy comparison, view-planning research, panoramic Gaussian-splatting reconstruction, panoramic world-model pretraining.

**Avoid:** identity-sensitive inference, safety-critical deployment, claims about private indoor spaces, treating synthetic-only results as real-world evidence without further validation.

### Distribution

Blender indoor frames (CC-BY 4.0), curator code (MIT), documentation (CC-BY 4.0), Datasheet, and Croissant metadata are released here. The four restricted sources ship metadata + regeneration scripts only.

### Maintenance

Versioned releases on a 6-month cadence. Errata are tracked via the project repository; checksum manifests are refreshed at every release; regeneration scripts are updated when upstream APIs, file layouts, or access terms change.

## Code

The full curator source code, adapters, and reproduction scripts are released as a separate, anonymized repository:

> **Code:** [`huggingface.co/anon-cmevs-2026/cmevs-code`](https://huggingface.co/anon-cmevs-2026/cmevs-code)

The `code/` subtree mirrored inside this dataset repository is provided for offline reviewer convenience; the linked code repository is the canonical source.

## Citation

```bibtex
@inproceedings{cmevs2026,
  title={{CM-EVS}: A Coverage-Curated Panoramic {RGB-D} Dataset for Indoor Scene Understanding},
  author={Anonymous Author(s)},
  booktitle={NeurIPS 2026 Datasets and Benchmarks Track (under review)},
  year={2026}
}
```

## Reviewer quick sample (NeurIPS 2026 D&B "large dataset URL" requirement)

The full Blender indoor archive is ~109 GB. To support reviewer-time inspection without a full download, **scene `sence_indoor_0001`** is provided as a representative sample at:

> [`huggingface.co/datasets/anon-cmevs-2026/cmevs-erp-eval/tree/main/blender_indoor/scenes/sence_indoor_0001`](https://huggingface.co/datasets/anon-cmevs-2026/cmevs-erp-eval/tree/main/blender_indoor/scenes/sence_indoor_0001)

This single scene contains 33 RGB panoramas (`panorama_NNNN.png`, 2048×1024 ERP), 33 range-depth arrays (`panorama_NNNN_depth.npy`, float32 metres), and 33 pose JSON files (`pose_NNNN.json`, scalar-first quaternion + position) — 99 files in total, ~250 MB.

### Sampling methodology

The scene was produced by the same end-to-end CM-EVS pipeline as every other Blender indoor scene in this release: asset loading → coordinate normalization to right-handed `+X`/`+Y`/`+Z` world frame → grid-based candidate generation with the 26-direction geometric-validity filter → conflict-aware greedy viewpoint selection → 2048×1024 Cycles ERP rendering → unified-schema export. No special preprocessing distinguishes the sample from the rest of the release; it was selected only because (i) it is the first scene id in lexical order and (ii) it represents the round1+2 sampling subset (the 201-scene half of the 374-scene v1.0 release; the other 173 scenes come from the round2 independent extraction). Reviewers can therefore use this scene to verify file format, coordinate convention, depth validity statistics, and image-depth alignment for the full release. The complete provenance — including the per-step coverage gain $G_t$, conflict ratio $L_t$, and viewpoint score $s_t$ — is in `metadata/per_step_log.jsonl` (curator-only fields, populated for all curator-produced frames once the §5 evaluation experiments are finalized).

## Notes on directory naming

Scene directories under `blender_indoor/scenes/` use the legacy id pattern `sence_indoor_NNNN` (note: `sence`, not `scene`). This is a typo inherited from the upstream Blender source pipeline used to produce the v1.0 build, and it is preserved verbatim so that scene ids match the production-side run logs and the entries in `metadata/scene_id_mapping.csv`. The misspelling **does not affect** file content, ERP coordinate convention, depth validity, pose schema, frame indexing, or downstream parsing — only the directory name string. Directories will be renamed to `scene_indoor_NNNN` in v1.1; the rename will be reflected in a new `scene_id_mapping.csv` row pointing each new id to its v1.0 `sence_indoor_NNNN` predecessor so existing consumers continue to resolve.

## Verifying integrity

```bash
# top-level files + adapter packages + code + metadata
shasum -a 256 -c SHA256SUMS

# Blender indoor frames (39,896 entries: 13,631 panorama + 12,634 depth + 13,631 pose)
cd blender_indoor && shasum -a 256 -c SHA256SUMS
```

## License

See `LICENSE.md` for the per-component license matrix.