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/
Transferable Unrestricted Attacks, which won 1st place in CVPR’21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

Transferable Unrestricted Adversarial Examples This is the PyTorch implementation of the Arxiv paper: Towards Transferable Unrestricted Adversarial Ex

equation 16 Dec 29, 2022
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code

sequitur sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code. It implements three differ

Jonathan Shobrook 305 Dec 21, 2022
This repository contains the code for Direct Molecular Conformation Generation (DMCG).

Direct Molecular Conformation Generation This repository contains the code for Direct Molecular Conformation Generation (DMCG). Dataset Download rdkit

25 Dec 20, 2022
This program generates a random 12 digit/character password (upper and lowercase) and stores it in a file along with your username and app/website.

PasswordGeneratorAndVault This program generates a random 12 digit/character password (upper and lowercase) and stores it in a file along with your us

Chris 1 Feb 26, 2022
State of the Art Neural Networks for Generative Deep Learning

pyradox-generative State of the Art Neural Networks for Generative Deep Learning Table of Contents pyradox-generative Table of Contents Installation U

Ritvik Rastogi 8 Sep 29, 2022
It's A ML based Web Site build with python and Django to find the breed of the dog

ML-Based-Dog-Breed-Identifier This is a Django Based Web Site To Identify the Breed of which your DOG belogs All You Need To Do is to Follow These Ste

Sanskar Dwivedi 2 Oct 12, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (AAAI 2021) Official pytorch implementation of our paper: Discriminative

Beom 74 Dec 27, 2022
PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

Unsupervised Depth Completion with Calibrated Backprojection Layers PyTorch implementation of Unsupervised Depth Completion with Calibrated Backprojec

80 Dec 13, 2022
Python script that allows you to automatically setup your Growtopia server.

AutoSetup Python script that allows you to automatically setup your Growtopia server. How To Use Firstly, install all the required modules that used i

Aspire 3 Mar 06, 2022
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
Camera-caps - Examine the camera capabilities for V4l2 cameras

camera-caps This is a graphical user interface over the v4l2-ctl command line to

Jetsonhacks 25 Dec 26, 2022
This repository contains the code used in the paper "Prompt-Based Multi-Modal Image Segmentation".

Prompt-Based Multi-Modal Image Segmentation This repository contains the code used in the paper "Prompt-Based Multi-Modal Image Segmentation". The sys

Timo Lüddecke 305 Dec 30, 2022
Yas CRNN model training - Yet Another Genshin Impact Scanner

Yas-Train Yet Another Genshin Impact Scanner 又一个原神圣遗物导出器 介绍 该仓库为 Yas 的模型训练程序 相关资料 MobileNetV3 CRNN 使用 假设你会设置基本的pytorch环境。 生成数据集 python main.py gen 训练

wormtql 18 Jan 08, 2023
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution SmallObject Detection

QueryDet-PyTorch This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small O

Chenhongyi Yang 276 Dec 31, 2022
The code succinctly shows how our ensemble learning based on deep learning CNN is used for LAM-avulsion-diagnosis.

deep-learning-LAM-avulsion-diagnosis The code succinctly shows how our ensemble learning based on deep learning CNN is used for LAM-avulsion-diagnosis

1 Jan 12, 2022
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset

MassiveSumm: a very large-scale, very multilingual, news summarisation dataset This repository contains links to data and code to fetch and reproduce

Daniel Varab 19 Dec 16, 2022
MutualGuide is a compact object detector specially designed for embedded devices

Introduction MutualGuide is a compact object detector specially designed for embedded devices. Comparing to existing detectors, this repo contains two

ZHANG Heng 103 Dec 13, 2022
This Repostory contains the pretrained DTLN-aec model for real-time acoustic echo cancellation.

This Repostory contains the pretrained DTLN-aec model for real-time acoustic echo cancellation.

Nils L. Westhausen 182 Jan 07, 2023