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Datasets:
CM-EVS-Dataset
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cmevs-erp-eval

Tasks:
Depth Estimation
Image-to-Image
Languages:
English
Size:
10K<n<100K
Tags:
panoramic
equirectangular
erp
rgb-d
viewpoint-selection
view-planning
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cmevs-erp-eval / code /README.md
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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 

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.