Kaiqi Wang, Ke Chen*, Kui Jia IJCAI 2019
[paper] | [project page]
This implementation uses Pytorch.
git clone https://github.com/wkqscut/DCGNet.git
cd DCGNet
## Create python env with relevant packages
conda create --name dcg python=3.7
conda activate dcg
pip install -U pip
pip install -r requirements.txt
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch # cudatoolkit=10.0 for cuda10
Tested on pytorch >= 1.0 and python3.
## Build chamfer distance
conda activate dcg
cd ./extension
python setup.py install
cd ../
We used the rendered imaged from 3d-R2N2, and the groundtruth 3D point clouds sampled from ShapeNet.
- The rendered images (*.png) go in
./data/ShapeNetRendering
- The ShapeNetPoints (*.mat) datasets go in
./data/ShapeNetPoints
- The pretrained models (*.pth) should be placed in
./trained_models/
## unzip the Pretrained models using the scripts
bash ./trained_models/unzip_models_dataset.sh
- demo code for DCGNet
bash ./scripts/demo.sh
Make sure that the visdom is alive before training:
python -m visdom.server -p 8990 (change the port if in use)
- train the DCGNet for Point Set AutoEncoding:
bash ./scripts/train_svr_dcg.sh
- train the DCGNet for Point Set Reconstruction from a Single Image:
bash ./scripts/train_svr_dcg.sh
- test the DCGNet for Point Set AutoEncoding:
bash ./scripts/test_svr_dcg.sh
- test the DCGNet for Point Set Reconstruction from a Single Image:
bash ./scripts/test_svr_dcg.sh
If you find this code useful for your research, please consider citing the following paper:
@inproceedings{ijcai2019-517,
title = {Deep Cascade Generation on Point Sets},
author = {Wang, Kaiqi and Chen, Ke and Jia, Kui},
booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-19}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {3726--3732},
year = {2019},
month = {7},
doi = {10.24963/ijcai.2019/517},
url = {https://doi.org/10.24963/ijcai.2019/517},
}
This work is supported in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No.: 2017ZT07X183), the National Natural Science Foundation of China (Grant No.: 61771201), and the Program of the Construction of Talented Personnel by the South China University of Technology (Grant No.: D6192110).