3D-CovDiffusion checkpoints

Category-specific tensor-only 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
• Dataset: https://huggingface.co/datasets/ChenyuanC/3D-CovDiffusion-Train-Ready

Released variants

Each root category contains the EMA policy selected by its original seed-42 run. raw/<category>/ contains the non-EMA state_dicts.model export used by the archived selected-visualization script. Here raw means policy weights before EMA, not raw dataset input or a Python/pickle checkpoint.

Every directory contains model.safetensors, config.yaml, and provenance metadata.

Directory	Dataset	Training run	Historical 6-D selection score ↓
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

The score above is the run's prediction-conditioned weighted 6-D pose Chamfer used for top-k selection. It includes XYZ and orientation normal and is not the final XYZ-only PCD or the paper's three-seed test result.

Selected case	Non-EMA 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

Containers is a separate low-data experiment. ODAV4-S42 is released because it is referenced by the archived in-domain, OOD, and video evaluation scripts.

Evaluate

From the code repository:

pip install -r requirements.txt
python reproduce.py prepare windows
python reproduce.py evaluate windows

prepare obtains the model, processed/canonical Hugging Face data, and the raw Zenodo mesh/trajectory/split records below one --artifact-root. The canonical cache locks the exact preprocessed model input; the raw records remain necessary for rollout geometry, ground truth, and metrics.

Use evaluate all for every released EMA policy. To replay the selected non-EMA case and automatically verify its numeric result:

python reproduce.py infer windows

Rendering is optional:

pip install -r requirements-visualization.txt
python reproduce.py infer windows --render

The PLY hash is a locked-environment render regression, not a requirement for numeric reproduction. The full checkpoint/config/test-index matrix and hashes are in docs/INFERENCE.md and configs/inference/seed42_selected_episodes.json in the code repository.

Format and integrity

• <category>/model.safetensors contains the complete EMA policy state.
• raw/<category>/model.safetensors contains the complete non-EMA policy state used for the selected-case replay.
• Both include action and point-cloud normalizer tensors.
• Optimizer state, Python/Dill training checkpoints, experiment logs, and machine-local paths are excluded.
• metrics.json records source run, epoch/step, historical selection score, source digest, release digest, and tensor-roundtrip validation.
• manifest.json and SHA256SUMS provide repository-wide integrity metadata.

Users never need to load the trusted historical pickle checkpoints; all public weights are safetensors.

Intended use and limitations

The policies generate ordered 6-DoF coverage-trajectory chunks from a 5,120-point observation and the previous 24-D action token (four ordered 6-DoF poses). They are research artifacts, not a certified motion-planning or robot- safety system. Validate collisions, kinematics, workcell constraints, and emergency behavior before physical deployment.

Only one selected seed-42 checkpoint is public per category. The paper's three-seed mean and standard deviation require the independent seed-123/456 checkpoints or their result JSON files.

License

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

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   = {Accepted at IROS 2026}
}