---
license: apache-2.0
language:
- en
tags:
- articulated object
size_categories:
- n<1K
---

# Dataset Card for XieNet

This is the **repaired** version of [GAPartNet](https://arxiv.org/abs/2211.05272) dataset, which we use as the simulation dataset for [Vi-TacMan](https://vi-tacman.github.io/).

## 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:

<div align="center">
  <table>
    <tr>
      <td align="center">
        <img src="figure/gapartnet_47296.png" width="300" alt="GAPartNet Original"/>
        <br/>
        GAPartNet (Original)
      </td>
      <td align="center">
        <img src="figure/xienet_47296.png" width="300" alt="XieNet Repaired"/>
        <br/>
        XieNet (Repaired)
      </td>
    </tr>
  </table>
</div>

We also provide the data generation code, which can be used to reproduce the simulated data presented in our paper [Vi-TacMan](https://vi-tacman.github.io/).

We sincerely thank the previous works ([SAPIEN](https://arxiv.org/abs/2003.08515), [PartNet](https://arxiv.org/abs/1812.02713), [GAPartNet](https://arxiv.org/abs/2211.05272)) and hope our repaired dataset can help advance this community.

## Usage

### Installation

First, install the required dependencies:

```bash
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

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

#### Full Command Line Options

```bash
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:

```bibtex
@inproceedings{cui2026vitacman,
  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},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2026},
  organization = {IEEE}    
}
```