https://github.com/lijx10/rot-equ-net

Discrete Rotation Equivariance for Point Cloud Recognition. ICRA 2019
https://github.com/lijx10/rot-equ-net

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Discrete Rotation Equivariance for Point Cloud Recognition. ICRA 2019

• Host: GitHub
• URL: https://github.com/lijx10/rot-equ-net
• Owner: lijx10
• License: mit
• Created: 2018-09-11T12:58:09.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-01T05:23:59.000Z (almost 6 years ago)
• Last Synced: 2024-08-01T03:45:49.387Z (5 months ago)
• Language: Python
• Homepage:
• Size: 5.06 MB
• Stars: 23
• Watchers: 5
• Forks: 8
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# Discrete Rotation Equivariance for Point Cloud Recognition

**Discrete Rotation Equivariance for Point Cloud Recognition.** ICRA 2019, Montreal, Canada.

Jiaxin Li, Yingcai Bi, Gim Hee Lee, National University of Singapore

## Introduction

This paper and repository propose a rotation equivariant method to improve deep network's performance 

when the input point cloud is rotated in 2D or 3D.

## Installation

Requirements:

- Python 3

- [PyTorch 0.4.1 or above](http://pytorch.org/)

- [Faiss](https://github.com/facebookresearch/faiss)

- [visdom](https://github.com/facebookresearch/visdom)

Optional dependency:

- Faiss [GPU support](https://github.com/facebookresearch/faiss/blob/master/INSTALL.md)

## Dataset

- ModelNet40 and MNIST

 Our dataset is provided by [SO-Net](https://github.com/lijx10/SO-Net):

  

  [Google Drive](https://drive.google.com/open?id=184MbflF_RbDX9MyML3hid7OxsYJ8oQQ7): MNIST, ModelNet40/10

 

## Usage

### Compile Cuda Operations

run `python3 setup.py install` in `models/index_mat_ext` to install the customized maxpooling layer.

### Configurations

To run these tasks, you may need to set the dataset type and path in `options.py`, by changing the default value of `--dataset`, `--dataroot`.

In training, you may want to configure the input rotation settings in `options.py`

- Input rotation augmentation mode: `rot_equivariant_mode`, supports `'2d' / '3d''` 

- Number of descrete rotation: `rot_equivariant_no`. Supports `{1, 4, 6, 9, 12, 24}` when `rot_equivariant_mode=2d`, 

`{1, 4, 12}` when `rot_equivariant_mode=3d`. 

### Visualization

We use visdom for visualization. Various loss values and the reconstructed point clouds (in auto-encoder) are plotted in real-time. Please start the visdom server before training, otherwise there will be warnings/errors, though the warnings/errors won't affect the training process.

```

python3 -m visdom.server

```

The visualization results can be viewed in browser with the address of:

```

http://localhost:8097

```

## License

This repository is released under MIT License (see LICENSE file for details).