This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

Related tags

Deep Learningaicomputer-vision3d
Overview

teaset

AtlasNet V2 - Learning Elementary Structures

This work was build upon Thibault Groueix's AtlasNet and 3D-CODED projects. (you might want to have a look at those)

This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

Citing this work

If you find this work useful in your research, please consider citing:

@inproceedings{deprelle2019learning,
  title={Learning elementary structures for 3D shape generation and matching},
  author={Deprelle, Theo and Groueix, Thibault and Fisher, Matthew and Kim, Vladimir and Russell, Bryan and Aubry, Mathieu},
  booktitle={Advances in Neural Information Processing Systems},
  pages={7433--7443},
  year={2019}
}

Project Page

The project page is available http://imagine.enpc.fr/~deprellt/atlasnet2/

Install

Clone the repo and install dependencies

This implementation uses Pytorch.

## Download the repository
git clone https://github.com/TheoDEPRELLE/AtlasNetV2.git
cd AtlasNetV2
## Create python env with relevant packages
conda create --name atlasnetV2 python=3.7
source activate atlasnetV2
pip install pandas visdom
conda install pytorch torchvision -c pytorch
conda install -c conda-forge matplotlib
# you're done ! Congrats :)

Training

Data

cd data; ./download_data.sh; cd ..

We used the ShapeNet dataset for 3D models.

When using the provided data make sure to respect the shapenet license.

The trained models and some corresponding results are also available online :

Build chamfer distance

The chamfer loss is based on a custom cuda code that need to be compile.

source activate pytorch-atlasnet
cd ./extension
python setup.py install

Start training

  • First launch a visdom server :
python -m visdom.server -p 8888
  • Check out all the options :
git pull; python training/train.py --help
  • Run the baseline :
git pull; python training/train.py --model AtlasNet --adjust mlp
git pull; python training/train.py --model AtlasNet --adjust linear
  • Run the Patch Deformation module with the different adjustment modules :
git pull; python training/train.py --model PatchDeformation --adjust mlp
git pull; python training/train.py --model PatchDeformation --adjust linear
  • Run the Point Translation module with the different adjustment modules:
git pull; python training/train.py --model PointTranslation --adjust mlp
git pull; python training/train.py --model PointTranslation --adjust linear

Models

The models train on the SURREAL dataset for the FAUST competition can be found here

Acknowledgement

This work was partly supported by ANR project EnHerit ANR-17-CE23-0008, Labex Bezout, and gifts from Adobe to Ecole des Ponts.

License

MIT

Comments
  • Unable to download shapenet data

    Unable to download shapenet data

    Hi,

    I am trying to download data form download.sh script. But it is giving 404 error.

    --2020-12-01 14:38:25-- https://cloud.enpc.fr/s/j2ECcKleA1IKNzk/download Resolving cloud.enpc.fr (cloud.enpc.fr)... 195.221.193.80 Connecting to cloud.enpc.fr (cloud.enpc.fr)|195.221.193.80|:443... connected. HTTP request sent, awaiting response... 404 Not Found 2020-12-01 14:38:26 ERROR 404: Not Found.

    could you please provide an alternative link?

    opened by brjathu 11
  • Question about evaluation critetion in paper?

    Question about evaluation critetion in paper?

    image Here, it is said that the reconstruction task is evaluated by chamfer distance. But for surreal data, the ground-truth correspondences are known. Why not just compute the L2 distance for correponding points?

    opened by GostInShell 3
  • How to Generate 16384 points for Point Translation Module?

    How to Generate 16384 points for Point Translation Module?

    As discussed in https://github.com/ThibaultGROUEIX/AtlasNet/issues/42, I want to upsample the results of the point translation module. Since this module takes a fixed number of points into the network. I don't know whether training a new model taking 16384 points as input is justifiable to compare with our method.

    opened by hzxie 2
  • The question about initialization of 'rand_grid' in the ./auxiliary/model.py

    The question about initialization of 'rand_grid' in the ./auxiliary/model.py

    I have a question. In the file 'model.py', line 378,379, why the variable 'rand_grid' is initialized to uniform(0,1) before it is initialized to zero. What is the reason? Thanks!

    bug 
    opened by tommaoer 2
  • Two bugs when running train.py

    Two bugs when running train.py

    First bug is

    Traceback (most recent call last):
  File "training/train.py", line 140, in <module>
    visdom = visdom.Visdom(env=opt.training_id, port=8888)
TypeError: __init__() got an unexpected keyword argument 'env'

    and I delete env=opt.training_id, then i re-run this code. And Second bug is

    Traceback (most recent call last):
  File "training/train.py", line 209, in <module>
    color =  [[125,125,125]]*(batch.size(1))
NameError: name 'batch' is not defined
    opened by Yuzuki-N 0
  • unused model in PointTransLinAdj

    unused model in PointTransLinAdj

    It seems that a deformation layer is defined and not used. https://github.com/TheoDEPRELLE/AtlasNetV2/blob/master/auxiliary/model.py#L302

    Did you intend to use this model?

    opened by orenkatzir 0
  • About visualization

    About visualization

    Hi, first thanks for your inspiring work! Point cloud rendering figures in your paper are beautiful as follows. How do you draw it? Using open3d, meshlab or other programmes?

    Thanks! image

    opened by StevenZzz07 0
  • Pretrained Models

    Pretrained Models

    Hi,

    I am trying to download data from https://cloud.enpc.fr/s/c27Df7fRNXW2uG3, but i get an 404 error. Could you please provide an alternative link? Thanks

    opened by rspezialetti 0
  • The problem of test.

    The problem of test.

    Dear professor, I have read the paper of " Learning Elementary Structures",and I have some problems. I have trained this network use datasets of Shapenet, and I get files of "network.pth" and "opt.pickle". But I can't find where is the "Elementary Structures" ,so I don't know how to compute correspondence use these "Elementary Structures". So I think your readme.md document is not complete, would you like to explain this issues.I don't know what to do after I finished trained my datasets, and how to get the correspondence. Looking for your early reply. Thank you!

    opened by cainiaoshidai 0
Releases(1-beta)
Owner
Théo Deprelle
Théo Deprelle
GitHub Repository http://imagine.enpc.fr/~deprellt/atlasnet2/
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