Skip to content

ada-shen/REQNN

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

3D-Rotation-Equivariant Quaternion Neural Networks (PyTorch)

Introduction

This repository is an official implementation of 3D-Rotation-Equivariant Quaternion Neural Networks (arXiv,SpringerLink) which has been published at ECCV 2020.

Note that, we have found a bug and solved it. Therefore, the experimental results obtained based on this repository are slightly different from that in the paper. However, this did not essentially change our conclusions. New experimental results are as follows.

Method ModelNet40 3D MNIST
Baseline w/o rotation Baseline w/ rotation REQNN(ours) Baseline w/o rotation Baseline w/ rotation REQNN(ours)
PointNet++ 25.87 29.25 62.03 44.19 51.48 72.01
DGCNN 32.08 33.78 84.57 45.90 50.00 84.57
PointConv 25.01 26.46 81.93 45.51 48.08 85.71

Besides, we have modified a few writing errors. In the second and third paragraph of the Experiment section, and the caption of Table 4, we have modified all “z-axis rotations” to “y-axis rotations”.

Installation

To run the program successfully, you need to include all packages in requirements.txt on your server.

pip install -r requirements.txt

Usage

To train a model to classify point clouds.

Run the training script:

python main.py 

Log files and network parameters will be saved to checkpoint folder in default.

You can specify models, datasets and other train configurations in training script. For example:

python main.py --exp_name=dgcnn_reqnn_1024_train --model=dgcnn_reqnn --dataset=modelnet --use_sgd=True --lr=0.001

See HELP for the training script:

python main.py -h

Run the evaluation script with trained models:

python main.py --exp_name=dgcnn_reqnn_1024_eval --eval=True --eval_model_path=your_trained_model_path

Citation

If you use this project in your research, please cite it.

@inproceedings{shen20193d,
	title={3d-rotation-equivariant quaternion neural networks},
	author={Shen, Wen and Zhang, Binbin and Huang, Shikun and Wei, Zhihua and Zhang, Quanshi},
	booktitle={ECCV},
	year={2020}
}

About

3D-Rotation-Equivariant Quaternion Neural Networks

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages