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3DNetworksPytorch

- Looking for new papers to implement in pytorch ! Go comment in the dedicated issue, papers you would like to see implemented using pytorch !!

This repository is mostly implementation of papers using the pytorch framework, PLEASE cite the corresponding papers before referencing to this work. User discretion is advised concerning accuracy and readiness of my implementations, please create issues when you encounter problems and I will try my best to fix them.

This repository is meant as way to learn by implementating them, different 3D deep learning architectures for pointclouds. I haven't tested them on benchmark datasets for the papers, only on some toy examples. If You spot any mistake, I am open to pull requests and any colaboration on the topic.

(I haven't cleaned the code completly so it might seem a bit messy at first sight) Most of the networks are using the cuda code in cppattempt. Please go in there and install the extension (python setup.py install), so that they can import it. The only things required should be pytorch 1.0+ and the corresponding cudatoolkit, everything configured correctly obviously. See pytorch explanations for how to compile C++ extensions.

Table of Contents

  1. PointSift
  2. PointCNN
  3. PointNet++
  4. Cuda_Extension
  5. 3D-BoNet
  6. SPGN
  7. PCN
  8. 3D_completion_challenge
  9. FastFCN 10.CSRNet

PointSift

An implementation of PointSift using Pytorch (https://arxiv.org/pdf/1807.00652.pdf) lies in the PoinSift folder. The C_utils folder contains some algorithms inplemented in CUDA and C++ taken from the original implementation of PointSift (https://github.com/MVIG-SJTU/pointSIFT) but wrapped to be used with Pytorch Tensor directly.

PointCNN

An implementation of PointCNN using Pytorch (https://arxiv.org/pdf/1801.07791.pdf) lies in the PointCNN folder.

PointNet++

An implementation of PointNet++ using Pytorch (https://arxiv.org/pdf/1706.02413.pdf) lies in the PointNet++ folder. It uses the same algorithms on GPU as PointSift as Pointsift uses Pointnet++ modules.

Cuda_Extension

There are two versions of the cuda extensions for pointnet and pointsift. The first one is in C_utils and was implemented using the old C api for torch. As it is now deprecated in newer version of pytorch and they recommend using the C++ extension api, I did an attempt in cppattempt folder.

3D-BoNet

Quick implementation of 3D-BoNet (https://arxiv.org/pdf/1906.01140.pdf) https://gist.github.com/lelouedec/5a7ba5547df5cef71b50ab306199623f using pytorch. All in one file, need to compile C++ pointnet extension. Code not converging for bounding boxes regressions

SPGN

Implementation of SGPN (https://arxiv.org/pdf/1711.08588.pdf) based on Pointnet implementation.

PCN

Implementation of PCN (PCN: Point Completion Network) (https://arxiv.org/pdf/1808.00671.pdf) (https://github.com/wentaoyuan/pcn) using pytorch. For the chamfer distance and the EMD loss, I used inplementation from respectively https://github.com/chrdiller/pyTorchChamferDistance and https://github.com/daerduoCarey/PyTorchEMD. See these repositories for how to use them. Copy emd.py and the compiled ".so" lib to the same directory of your model and it should be fine. Tested with the PCN paper shapenet data, download it from the google drive provided in their repository. The dataloader will help loading the pointclouds from the shapenet directory. See following screenshot for example (Left is groundtruth, middle the input and right the output of the network): Example for pcn

3D_completion_challenge

A new 3D completion challenge is available here : https://github.com/lynetcha/completion3d it includes PCN (seen above)

FastFCN

Two files implementation of the fast fcn paper based on their own implementation. Go check the paper and git for more details : https://arxiv.org/pdf/1903.11816.pdf https://github.com/wuhuikai/FastFCN

CSRNet

Implementation of the model used in the paper : https://arxiv.org/pdf/1802.10062.pdf. It is only one of the variation but the one used and advertised by the author. The output as in the paper needs to be upsampled to compare to the original image.

As asked:

@software{lelouedec_2020_3766070,
  author       = {lelouedec},
  title        = {lelouedec/3DNetworksPytorch: pre-alpha},
  month        = apr,
  year         = 2020,
  version      = {0.1},
}