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PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data

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Zhe Zhu¹, Le Wan², Rui Xu³, Yiheng Zhang⁴, Honghua Chen⁵, Zhiyang Dou³, Cheng Lin⁶, Yuan Liu^2†, Mingqiang Wei^1† <br> † Corresponding authors

¹ NUAA ² HKUST ³ HKU ⁴ NUS ⁵ LU ⁶ MUST

<p align="center"> <a href="https://czvvd.github.io/PartSAMPage/"> <img src="https://img.shields.io/badge/Project%20Page-blue.svg" alt="Project Page" height="22"> </a> <a href="https://arxiv.org/abs/2509.21965"> <img src="https://img.shields.io/badge/arXiv-b31b1b.svg?logo=arXiv&logoColor=white" alt="arXiv height="22"> </a> </p> </div> <p align="center"> <img src="assets/teaser.png" alt="teaser"> </p>

Installation

1. Install the required environment

conda create -n PartSAM python=3.11 -y
conda activate PartSAM
# PyTorch 2.4.1 with CUDA 12.4
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
pip install lightning==2.2 h5py yacs trimesh scikit-image loguru boto3
pip install mesh2sdf tetgen pymeshlab plyfile einops libigl polyscope potpourri3d simple_parsing arrgh open3d safetensors
pip install hydra-core omegaconf accelerate timm igraph ninja
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.4.1+cu124.html
apt install libx11-6 libgl1 libxrender1
pip install vtk

2. Install the other third-party dependencies as follows:

• torkit3d and apex: follow the installation instructions provided in Point-SAM.
• pointops: install according to SAMPart3D.

3. Install the pretrained model weight

pip install -U "huggingface_hub[cli]"
huggingface-cli login
huggingface-cli download Czvvd/PartSAM --local-dir ./pretrained

Usage

# Modify the config file to evaluate your own meshes
python evaluation/eval_everypart.py

TODO

• [x] Release inference code of PartSAM
• [x] Release the pre-trained models
• [ ] Release training code and data processing script

Acknowledgement

Our code is based on these wonderful works:

• Point-SAM
• PartField
• SAMPart3D
• SAMesh
• Find3D

We thank the authors for their great work！

Citation

@article{zhu2025partsam,
  title={PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data}, 
  author={Zhe Zhu and Le Wan and Rui Xu and Yiheng Zhang and Honghua Chen and Zhiyang Dou and Cheng Lin and Yuan Liu and Mingqiang Wei},
  journal={arXiv preprint arXiv:2509.21965},
  year={2025}
}