AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim , Bryan C. Russell, Mathieu Aubry
In CVPR, 2018.
:rocket: New branch : AtlasNet + Shape Reconstruction by Learning Differentiable Surface Representations
This implementation uses Python 3.6, Pytorch, Pymesh, Cuda 10.1.
# Copy/Paste the snippet in a terminal
git clone --recurse-submodules https://github.com/ThibaultGROUEIX/AtlasNet.git
cd AtlasNet
#Dependencies
conda create -n atlasnet python=3.6 --yes
conda activate atlasnet
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch --yes
pip install --user --requirement requirements.txt # pip dependencies
# Copy/Paste the snippet in a terminal
python auxiliary/ChamferDistancePytorch/chamfer3D/setup.py install #MIT
cd auxiliary
git clone https://github.com/ThibaultGROUEIX/metro_sources.git
cd metro_sources; python setup.py --build # build metro distance #GPL3
cd ../..
Data download should be automatic. However, due to the new google drive traffic caps, you may have to download manually. If you run into an error running the demo, you can refer to #61.
You can manually download the data from three sources (there are the same) :
Please make sure to unzip the archives in the right places :
cd AtlasNet
mkdir data
unzip ShapeNetV1PointCloud.zip -d ./data/
unzip ShapeNetV1Renderings.zip -d ./data/
unzip metro_files.zip -d ./data/
unzip trained_models.zip -d ./training/
python train.py --demo
python train.py --shapenet13
Monitor on http://localhost:8890/Method | Chamfer (*1) | Fscore (*2) | Metro (*3) | Total Train time (min) |
---|---|---|---|---|
Autoencoder 25 Squares | 1.35 | 82.3% | 6.82 | 731 |
Autoencoder 1 Sphere | 1.35 | 83.3% | 6.94 | 548 |
SingleView 25 Squares | 3.78 | 63.1% | 8.94 | 1422 |
SingleView 1 Sphere | 3.76 | 64.4% | 9.01 | 1297 |
@inproceedings{groueix2018,
title={{AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation}},
author={Groueix, Thibault and Fisher, Matthew and Kim, Vladimir G. and Russell, Bryan and Aubry, Mathieu},
booktitle={Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}