Dataset Card for XieNet

This is the repaired version of GAPartNet dataset, which we use as the simulation dataset for Vi-TacMan.

Description

We identified numerous object meshes in the original dataset that lack proper cap geometry, so we manually repaired these meshes to ensure completeness. The following images (object id: 47296) exemplify the type of geometric defects found and our corrections:

GAPartNet (Original)

XieNet (Repaired)

We also provide the data generation code, which can be used to reproduce the simulated data presented in our paper Vi-TacMan.

We sincerely thank the previous works (SAPIEN, PartNet, GAPartNet) and hope our repaired dataset can help advance this community.

Usage

Installation

First, install the required dependencies:

pip install -r requirements.txt

Requirements:

• Python 3.10
• SAPIEN 3.0.1

Data Generation

The main script main.py generates simulated data by rendering articulated objects from multiple camera viewpoints with different articulation states.

Basic Usage

python main.py \
    --data_root_dir /path/to/XieNet \
    --save_dir /path/to/output/directory

Full Command Line Options

python main.py \
    --data_root_dir /path/to/XieNet/dataset \  # Path to the XieNet dataset root
    --save_dir /path/to/output/directory \     # Output directory for rendered data
    --seed 42 \                                # Random seed (default: 42)
    --render_width 640 \                       # Render width (default: 640)
    --render_height 576 \                      # Render height (default: 576)
    --fovy 65.0 \                              # Field of view in degrees (default: 65.0)
    --near 0.01 \                              # Near clipping plane (default: 0.01)
    --far 4.0 \                                # Far clipping plane (default: 4.0)
    --enable_rt \                              # Enable ray tracing (optional)
    --min_movable_area 4096 \                  # Minimum area for movable parts (default: 4096)
    --max_flow_dist 0.1 \                      # Maximum flow distance (default: 0.1)
    --save_vis                                 # Save visualization images (default: True)

Supported Object Categories

The data generation focuses on the following articulated object categories, for which we provide repaired meshes:

• Dishwasher
• Door
• Microwave
• Oven
• Refrigerator
• Safe
• StorageFurniture
• Table
• Toilet
• TrashCan
• WashingMachine

Output Data Format

For each object and camera viewpoint, the script generates:

• pcd_camera.npy: Structured numpy array containing:
  • point: 3D point coordinates in camera frame
  • rgb: RGB color values
  • articulation_flow: 3D flow vectors for articulation motion
  • mask_holdable: Binary mask for holdable parts
  • mask_movable: Binary mask for movable parts
  • mask_ground: Binary mask for ground plane
• camera_pose.txt: 4x4 camera pose matrix
• camera_intrinsics.txt: 3x3 camera intrinsic matrix
• vis/ folder (if --save_vis is enabled): Visualization images including color, depth, masks, and flow visualizations

Citation

If you find this dataset beneficial, please cite our research paper as follows:

@article{cui2025vitacman,
    title={Vi-{T}ac{M}an: Articulated Object Manipulation via Vision and Touch},
    author={Cui, Leiyao and Zhao, Zihang and Xie, Sirui and Zhang, Wenhuan and Han, Zhi and Zhu, Yixin},
    journal={arXiv preprint arXiv:2510.06339},
    year={2025}
}