raw/README.md

metadata

library_name: pytorch
tags:
  - robotics
  - diffusion-policy
  - 3d-point-cloud
  - coverage-path-planning
  - safetensors

3D-CovDiffusion checkpoints

Category-specific pretrained policies for 3D-CovDiffusion: 3D-Aware Diffusion Policy for Coverage Path Planning.

• Project page: https://crystalccy1.github.io/3D-CovDiffusion/
• Code: https://github.com/crystalccy1/3D-CovDiffusion
• Train-ready dataset: https://huggingface.co/datasets/ChenyuanC/3D-CovDiffusion-Train-Ready

Released models

Each root category directory contains the EMA weights selected by the original seed-42 training run. raw/<category>/ contains a tensor-only exact export of the raw state_dicts.model entry loaded by the archived selected-visualization test script. Every directory includes its inference configuration and provenance metadata.

Directory	Dataset	Training run	Top-k selection PCD ↓
windows/	windows-v2	TML4Q-S42	10.410878
cuboids/	cuboids-v2	X1PD1-S42	6.612324
shelves/	shelves-v2	52VCU-S42	10.069027
containers/	containers-v2	ODAV4-S42	347.932620

Selected inference case	Raw tensor path	Test split item
Windows	raw/windows/model.safetensors	index 5, 810_wr1fr_1
Cuboids	raw/cuboids/model.safetensors	index 3, 669_cube_1001_1285_1263
Shelves	raw/shelves/model.safetensors	index 4, box_h620_w500_d220.0_sh1.0_sv2.0
Containers	raw/containers/model.safetensors	index 1, spoegcr3gv

The values above are the per-run validation monitor used for top-k checkpoint selection. They are not the three-seed test results reported in the paper. Containers is a separate low-data experiment; ODAV4-S42 is released because it is the checkpoint referenced by the original in-domain, OOD, and video evaluation scripts.

Download and evaluate

From the code repository, download one category plus release manifests:

python reproduce.py download --category windows
python reproduce.py smoke --category windows --device cuda

With the separate raw evaluation meshes, trajectories, and fixed splits:

python reproduce.py evaluate \
  --category windows \
  --eval-root /absolute/path/to/evaluation-data \
  --episodes 0

Replace windows with cuboids, shelves, or containers. Numeric evaluation is metrics-only by default. The train-ready dataset is train-only and cannot be used as the evaluation root.

To reproduce one selected project-page visualization with the exact raw tensor variant, use the tagged inference-v1 code release. It replays the archived GT_Cond -> Pred_Cond RNG order and reports the prediction-conditioned result:

python reproduce.py download \
  --category windows \
  --weight-variant raw \
  --models-only

python reproduce.py inference \
  --category windows \
  --eval-root /absolute/path/to/raw-evaluation-data \
  --save-artifacts

The full checkpoint/config/test-index matrix and SHA-256 regression hashes are in the code repository's docs/INFERENCE.md and configs/inference/seed42_selected_episodes.json.

Format and integrity

• Root <category>/model.safetensors files contain complete EMA policy states.
• raw/<category>/model.safetensors files contain the complete raw policy states used by the archived selected-visualization tests.
• Both variants include the action and point-cloud normalizer tensors.
• Optimizer state, full Python/Dill training checkpoints, W&B metadata, and machine-local paths are intentionally excluded.
• metrics.json records the source run, epoch, step, selection metric, source checkpoint digest, release digest, and exact tensor-roundtrip validation.
• manifest.json and SHA256SUMS provide repository-wide integrity metadata.

The original training checkpoints must be treated as trusted pickle files. All files in this repository use tensor-only safetensors; users never need to load the source .ckpt files.

Intended use and limitations

These checkpoints generate ordered 6-DoF coverage-trajectory chunks from a 5,120-point observation and recent execution history. They are research artifacts evaluated on the corresponding geometry categories; they are not a certified motion-planning or robot-safety system. Validate collision handling, kinematic feasibility, workcell constraints, and emergency behavior before any physical deployment.

Only the selected seed-42 checkpoint is currently released for each category. The paper's three-seed mean and standard deviation cannot be regenerated until the other checkpoints or their per-seed result JSON files are published.

License

No standalone repository or model-weight license has been selected yet. Third-party components remain subject to their original terms; see the notices in the code repository.

Citation

@misc{chen2026_3dcovdiffusion,
  title  = {{3D-CovDiffusion}: 3D-Aware Diffusion Policy for Coverage Path Planning},
  author = {Chen, Chenyuan and Ding, Haoran and Ding, Ran and Liu, Tianyu
            and He, Zewen and Duan, Anqing and Nakamura, Yoshihiko},
  year   = {2026},
  note   = {Manuscript}
}