AtlasNet (http://imagine.enpc.fr/~groueixt/atlasnet/) (https://arxiv.org/abs/1802.05384) (http://imagine.enpc.fr/~groueixt/atlasnet/atlasnet_slides_spotlight_CVPR.pptx)
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.
Install
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
Optional : Compile Chamfer (MIT) + Metro Distance (GPL3 Licence)
# 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 ../..
Usage
- Demo :
python train.py --demo - Training :
python train.py --shapenet13Monitor on http://localhost:8890/
Quantitative Results, 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, * (*1) x1000. Computed between 2500 ground truth points and 2500 reconstructed points.
- (*2) The threshold is 0.001
- (*3) x100. Metro is ran on unormalized point clouds (which explains a difference with the paper's numbers)
Related projects
- Learning Elementary Structures
- 3D-CODED
- Cycle Consistent Deformations
- Atlasnet code V2.2 (more linear, script like, may be easier to understand at first)
Citing this work
@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}
}