Update dataset card with paper/code links, authors, and sample usage

README.md

@@ -1,13 +1,14 @@
 ---
-license: other
-license_name: cm-evs-mixed
 language:
 - en
+license: other
 size_categories:
 - 10K<n<100K
 task_categories:
 - depth-estimation
 - image-to-image
+pretty_name: CM-EVS — Coverage-Curated Panoramic RGB-D Dataset
+license_name: cm-evs-mixed
 tags:
 - panoramic
 - equirectangular
@@ -20,11 +21,12 @@ tags:
 - 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
 
+[**Paper**](https://huggingface.co/papers/2605.15597) | [**Code**](https://github.com/Strange-animalss/CM-EVS)
+
 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.
@@ -41,6 +43,24 @@ CM-EVS is a curated panoramic RGB-D dataset built under a single principle: **ma
 
 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.
 
+## Sample Usage
+
+To run the full Blender-indoor pipeline using the code from the official repository:
+
+```bash
+export PYTHONPATH="$PWD:$PWD/pipelines:${PYTHONPATH:-}"
+
+python3 pipelines/run_full_pipeline.py \
+  --blender /path/to/blender \
+  --input-dir data/blender_indoor \
+  --output-root outputs/blender_indoor \
+  --num-frames 30 \
+  --resolution 2048,1024 \
+  --grid-spacing 0.5 \
+  --min-frames 5 \
+  --stop-gain 0.08
+```
+
 ## Output schema
 
 Every released ERP frame follows a single coordinate convention:
@@ -57,8 +77,6 @@ Every released ERP frame follows a single coordinate convention:
 | `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
 
 ```
@@ -86,14 +104,12 @@ cmevs_hf_release/
 
 ## Datasheet
 
-Following Gebru et al. 2021. (Source: `main.tex` Appendix A — content here is a faithful markdown rendering; cross-check against the paper PDF.)
+Following Gebru et al. 2021. (Source: `main.tex` Appendix A — content here is a faithful markdown rendering.)
 
 ### 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.
@@ -104,7 +120,7 @@ Following Gebru et al. 2021. (Source: `main.tex` Appendix A — content here is
 
 **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).
+**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`.
 
 **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).
 
@@ -132,26 +148,24 @@ Versioned releases on a 6-month cadence. Errata are tracked via the project repo
 
 ## 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 curator source code, adapters, and reproduction scripts are released in the following repository:
 
-The `code/` subtree mirrored inside this dataset repository is provided for offline reviewer convenience; the linked code repository is the canonical source.
+> **GitHub:** [https://github.com/Strange-animalss/CM-EVS](https://github.com/Strange-animalss/CM-EVS)
 
 ## Citation
 
 ```bibtex
-@inproceedings{cmevs2026,
-  title={{CM-EVS}: A Coverage-Curated Panoramic {RGB-D} Dataset for Indoor Scene Understanding},
-  author={Anonymous Author(s)},
+@inproceedings{liu2026cmevs,
+  title={CM-EVS: Sparse Panoramic RGB-D-Pose Data for Complete Scene Coverage},
+  author={Jiale Liu and Jungang Li and Jieming Yu and Xinglin Yu and Zihao Dongfang and Zongjian Ding and Kaifeng Ding and Yi Yang and Lidong Chen and Yang Zou and Shunwen Bai and Jiahuan Zhang and Haoran Huang and Shan Huang and Yudong Gao and Mingjun Cheng},
   booktitle={NeurIPS 2026 Datasets and Benchmarks Track (under review)},
   year={2026}
 }
 ```
 
-## Reviewer quick sample (NeurIPS 2026 D&B "large dataset URL" requirement)
+## Reviewer quick sample
 
-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:
+The full Blender indoor archive is ~109 GB. To support 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)
 
@@ -159,11 +173,11 @@ This single scene contains 33 RGB panoramas (`panorama_NNNN.png`, 2048×1024 ERP
 
 ### 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).
+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.
 
 ## 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.
+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`. Directories will be renamed to `scene_indoor_NNNN` in v1.1.
 
 ## Verifying integrity
 
@@ -177,4 +191,4 @@ cd blender_indoor && shasum -a 256 -c SHA256SUMS
 
 ## License
 
-See `LICENSE.md` for the per-component license matrix.
+See `LICENSE.md` for the per-component license matrix.