https://github.com/XuyangBai/KPConv.pytorch

PyTorch reimplementation for "KPConv: Flexible and Deformable Convolution for Point Clouds" https://arxiv.org/abs/1904.08889
https://github.com/XuyangBai/KPConv.pytorch

3d-point-clouds geometric-deep-learning kpconv pytorch

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PyTorch reimplementation for "KPConv: Flexible and Deformable Convolution for Point Clouds" https://arxiv.org/abs/1904.08889

• Host: GitHub
• URL: https://github.com/XuyangBai/KPConv.pytorch
• Owner: XuyangBai
• Created: 2019-12-18T13:40:57.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-07-09T01:26:17.000Z (almost 4 years ago)
• Last Synced: 2024-03-12T18:32:18.674Z (4 months ago)
• Topics: 3d-point-clouds, geometric-deep-learning, kpconv, pytorch
• Language: C++
• Homepage:
• Size: 110 KB
• Stars: 62
• Watchers: 6
• Forks: 9
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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• awesome-stars - XuyangBai/KPConv.pytorch - PyTorch reimplementation for "KPConv: Flexible and Deformable Convolution for Point Clouds" https://arxiv.org/abs/1904.08889 (C++)

README

        
## KPConv.pytorch

This repo is implementation for KPConv(https://arxiv.org/abs/1904.08889) in pytorch.

## TODO

There are still some works to be done:

- [x] Deformable KPConv. Currently I have only implemented the rigid KPConv.

  - [ ] Regularization loss for the deformable convolution needs to be implemented. I have tried using the deformable convolution layer in part segmention on shapenet without the regularization term, the performance is similar with the rigid convolution counterparts.

- [x] Speed up. For current implementation, the `collate_fn` where the neighbor indices and pooling indices are calculated, is too slow. In the tf version, the author implement 2 tensroflow C++ wrapper which is quite efficient. I am planing to write C++ extention using pytorch. 

  - [ ] But after I implemented the C++ extention, the evaluation time reduces significantly while the model forward and backward pass still cost about 0.8s per iteration.

- [ ] Maybe other datasets.

## Installation

1. Create an environment from the environment.yml file,

```

conda env create -f environment.yml

```

2. Compile the customized Tensorflow operators and C++ extension module following the [installation instructions](https://github.com/HuguesTHOMAS/KPConv/blob/master/INSTALL.md) provided by the authors.

3. Go to `pytorch_ops` dictionary and run `python setup.py install` to build and install the C++ extension for `batch_find_neighbors` function.

## Experiments

Due to the time limitation, I have just implemented the experiments on ShapeNet(classification and part segmentation) and ModelNet40. 

- Shape Classification on ModelNet40 or ShapeNet.

```

python training_ModelNet.py[training_ShapeNetCls.py]

```

- Part Segmentation on ShapeNet. (I have only implemented the single class part segmentation.)

```

python training_ShapeNetPart.py

```

## Acknowledgment

Thank @HuguesTHOMAS for sharing the tensorflow version and valuable explainations.