ChenyuanC/3D-CovDiffusion-Train-Ready

3D-CovDiffusion Train-Ready Dataset

Processed artifacts for 3D-CovDiffusion: compact training tensors and self-contained evaluation inputs for the complete fixed test splits. Users do not need to rebuild point clouds, trajectories, or mesh geometry from raw records.

Contents

Category	Training episodes	Training steps	Evaluation cases
Windows	800	161,436	200
Cuboids	800	486,169	200
Shelves	800	459,439	200
Containers	70	37,175	18
Total	2,470	1,144,219	618

3D-CovDiffusion-Train-Ready/
├── dataset_manifest.json
├── evaluation_cache_manifest.json
├── data/
│   ├── windows-v2/{manifest.json,train.zarr/}
│   ├── cuboids-v2/{manifest.json,train.zarr/}
│   ├── shelves-v2/{manifest.json,train.zarr/}
│   └── containers-v2/{manifest.json,train.zarr/}
└── evaluation-cache/
    ├── windows-v2/*.npz
    ├── cuboids-v2/*.npz
    ├── shelves-v2/*.npz
    └── containers-v2/*.npz

Training schema

Each train.zarr uses schema 3dcov-train-v1:

train.zarr/
├── meta/episode_ends       int64   [episodes]
├── data/action             float32 [steps, 24]
├── data/stroke_ids         float32 [steps]
└── obs/point_cloud         float32 [episodes, 5120, 3]

Every 24-D action token concatenates four ordered 6-DoF poses. The model trains on horizon-16 token sequences. Each static 5,120-point observation is stored once per episode.

Evaluation schema

The 3dcov-evaluation-ready-v2 manifest stores the ordered fixed-test sample IDs and a SHA-256 digest for every NPZ. Each NPZ uses schema 3dcov-evaluation-v2 and contains:

schema_version  scalar string
sample_id       scalar string
point_cloud     float [5120, 3]
trajectory      float [T, 24]
gt_trajectory   float [P, 6]
stroke_ids      int   [P]
mesh_vertices   float [V, 3]
mesh_faces      int   [F, 3]

These 618 files contain the complete model and metric inputs. The released loader performs validation only; it does not resample meshes or rebuild action tokens at runtime.

Download and use

The public code pins an immutable dataset revision:

git clone https://github.com/crystalccy1/3D-CovDiffusion.git
cd 3D-CovDiffusion
python -m pip install -r requirements.txt

python reproduce.py prepare windows --data-only
python reproduce.py prepare windows --checkpoint-only
python reproduce.py evaluate windows

Use cuboids, shelves, or containers for another category.

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}
}

Attribution and license

This work uses modified data derived from “MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds” by Gabriele Tiboni, Zenodo record 14967945, licensed under CC BY 4.0. The data were converted and preprocessed by the 3D-CovDiffusion authors; no endorsement by the original creator is implied.