RMA-Net

This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021).

Paper address: https://arxiv.org/abs/2011.12104

Project webpage: https://wanquanf.github.io/RMA-Net.html

Prerequisite Installation

The code has been tested with Python3.8, PyTorch 1.6 and Cuda 10.2:

conda create --name rmanet
conda activate rmanet
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install -c conda-forge igl

Build the cuda extension:

python build_cuda.py

Usage

Pre-trained Models

Download the pre-trained models and put the models in the [YourProjectPath]/pre_trained folder.

Run the registration

To run registration for a single sample, you can run:

python inference.py --weight [pretrained-weight-path] --src [source-obj-path] --tgt [target-obj-path] --iteration [iteration-number] --device_id [gpu-id] --if_nonrigid [1 or 0]

The last argument --if_nonrigid represents if the translation between the source and target is non-rigid (1) or rigid (0). Registration results are listed in the folder named source_deform_results, including the deforming results of different stages. We have given a collection of samples in [YourProjectPath]/samples, and you can run the registration for them by:

sh inference_samples.sh

Datasets

To show how to construct a dataset that can be used in the code, we list some toy pairs in the [YourProjectPath]/toy_dataset folder and give a script to pack them into a bin file:

code for constructing a dataset

Or you can also download the dataset we used in the paper here.

Train & Test

To test on the whole testing set, run:

code for testing on the whole dataset

To train the network, run:

code for training the network

Citation

Please cite this paper with the following bibtex:

@inproceedings{feng2021recurrent,
    author    = {Wanquan Feng and Juyong Zhang and Hongrui Cai and Haofei Xu and Junhui Hou and Hujun Bao},
    title     = {Recurrent Multi-view Alignment Network for Unsupervised Surface Registration},
    booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2021}
}