CSGStumpNet
The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Paper | Project page
Citation
If you find our work interesting and benifits your research, please consider citing:
@article{ren2021csgstump,
title={CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing},
author={Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Haiyong Jiang, Zhongang Cai, Junzhe Zhang, Liang Pan, Mingyuan Zhang, Haiyu Zhao, Shuai Yi},
journal={ICCV},
year={2021}
}
Setup
Install envoriment:
We recommand using Anaconda to set the envoriment, once Anacodna in installed, run the following command.
conda create --name CSGStumpNet python=3.7
conda activate CSGStumpNet
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
conda install -c open3d-admin open3d=0.9
conda install numpy
conda install pymcubes
conda install tensorboard
conda install scipy
pip install tqdm
Datasets and pre-trained weights
Dataset
We use the pre-prepared dataset from OccNet(consider citing them), you can download the data by
mkdir data
cd data
wget https://s3.eu-central-1.amazonaws.com/avg-projects/occupancy_networks/data/dataset_small_v1.1.zip
unzip dataset_small_v1.1.zip
If you want to prepare data yourself (maybe you want to generate the watertight mesh etc.), please refer to this link.
Pre-Train Weights
The original trained weights are no longer compatiable with the the restructred code (this repo) ? re-training is in progress (This may take some time).
Evaluate using pre-trian weights
python eval.py --config_path ./configs/plane_256_256.json
Train from stratch
python train.py --config_path ./configs/plane_256_256.json
Evaluation
python metrics.py --config_path ./configs/plane_256_256.json
License
This project is licensed under the terms of the MIT license (see LICENSE for details).
