Score-PA: Score-based 3D Part Assembly
This is the official implementation of the paper "Score-PA: Score-based 3D Part Assembly", BMVC 2023 (Oral).
Authors: Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu and Hao Dong.
Installation
Requirements
- Python 3.8.5
- PyTorch 1.9.1 + CUDA 11.1
- PyTorch Geomtric 2.0.3
- PyTorch3D 0.6.1
Install Chamfer Distance library
cd ./cd
python setup.py install
Datasets
We follow Generative 3D Part Assembly via Dynamic Graph Learning to use their preprocessed datasets. Here is the download link.
Usage
We have provided the training and testing scripts for the three datasets (chair, table and lamp) in the folder "scripts". You can directly run the scripts to train and test the model. Please strictly use the parameters in the scripts to reproduce the results in the paper.
Training
sh train_{chair / table / lamp}.sh
Testing
sh test_{chair / table / lamp}.sh
Pretrained Models
We release our pretrained models in the "pretrained_models" folder.
Citation
@article{cheng2023score,
title={Score-PA: Score-based 3D Part Assembly},
author={Cheng, Junfeng and Wu, Mingdong and Zhang, Ruiyuan and Zhan, Guanqi and Wu, Chao and Dong, Hao},
journal={British Machine Vision Conference (BMVC)},
year={2023}
}
Acknowledgement
Our code is inspired by Score-based Generative Modeling through Stochastic Differential Equations and Generative 3D Part Assembly via Dynamic Graph Learning. We thank the authors for their great works.