# CM-EVS Curator Code

**Paper:** CM-EVS: Sparse Panoramic RGB-D-Pose Data for Coverage-Oriented Scene Observation

**Authors:** Jiale Liu, Jungang Li, Jieming Yu, Xinglin Yu, Zihao Dongfang, Keyu Shi, Zongjian Ding, Jiahuan Zhang, Shunwen Bai, Haoran Huang, Yurun Wang, Yanxi Wu, Ningzhe Yu, Yudong Gao, Mingjun Cheng

This directory mirrors the curator source code from the public CM-EVS code release. It's included here so reviewers can cross-reference implementation details against the paper without needing two URLs.

## Layout

```
code/
├── core/                       (curator core modules — coordinate, ERP projection / warping,
│                                tangent extraction, depth fusion, …)
├── scripts/                    (per-step CLI scripts — build_candidates, select_views,
│                                render_selected, evaluate_coverage, evaluate_oracle_gap,
│                                audit_quality, run_tiny.sh, …)
├── pipelines/                  (per-source end-to-end runners — Blender, HM3D,
│                                ScanNet++, full multi-source pipeline)
├── tools/                      (helper tools — make_sha256sums.sh,
│                                update_croissant_with_real_hashes.py, …)
├── configs/                    (per-source YAML configs used by pipelines)
├── metadata_examples/          (JSON schemas: candidates, selected_viewpoints,
│                                per_step_log)
├── examples/tiny_blender_scene/  (smoke-test fixture — run scripts/run_tiny.sh)
├── dataset_metadata/           (croissant.json reference copy; same as top-level
│                                ../croissant.json)
├── environment.yml             (conda environment)
├── requirements.txt            (pip fallback)
└── LICENSE                     (MIT)
```

## Quick start

```bash
conda env create -f environment.yml
conda activate cmevs

# tiny smoke test (uses examples/tiny_blender_scene/)
bash scripts/run_tiny.sh

# real run:
python pipelines/run_blend_pipeline.py    --config configs/blender_indoor.yaml
python pipelines/run_hm3d_pipeline.py     --config configs/hm3d.yaml
python pipelines/run_ply_pipeline.py      --config configs/scannetpp.yaml
```

## Mapping paper sections → code

| Paper section | Code |
| --- | --- |
| §3.1 Setup (candidates, budget, coverage) | `scripts/build_candidates.py`, `core/coordinate.py` |
| §3.2 Conflict-aware warping oracle | `core/erp_warp.py`, `scripts/selection_metrics.py` |
| §3.3 Pipeline three phases | `pipelines/run_blend_pipeline.py`, `run_hm3d_pipeline.py`, `run_ply_pipeline.py` |
| §3.4 Adapters (Table 2) | `pipelines/run_*_pipeline.py` per source |
| Algorithm 1 (greedy) | `scripts/select_views.py` |
| §3.6 Adaptive frame budgets (early stop) | `scripts/select_views.py` (`stop_gain`, `stop_score`, `stop_delta` flags) |
| §4.1 Output schema | `core/coordinate.py`, `core/erp_projection.py`, `metadata_examples/*.schema.json` |
| §4.8 per-frame quality | `scripts/audit_quality.py` |
| §5.1 Fixed-budget coverage | `scripts/evaluate_coverage.py` |
| §5.2 Warping oracle vs pre-render-all | `scripts/evaluate_oracle_gap.py` |
| §F.2 50-frame audit | `scripts/audit_quality.py` (extended audit mode) |

## Reproducibility

All curator parameters that affect paper numbers are in `configs/*.yaml`. The reviewer can:

1. Inspect each YAML to confirm parameter values match the paper.
2. Run a smoke test against `examples/tiny_blender_scene/` to confirm the code executes end-to-end.
3. Reproduce the full pipeline against actual data (Blender indoor frames are in `../blender_indoor/`; HM3D / ScanNet++ / outdoor data must be obtained from upstream).

## License

MIT — see `LICENSE` and the top-level `../LICENSE.md` for the full per-component matrix.