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 torchvision 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 ../..
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}
}