Awesome
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 Python 3.8, 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
Other requirements include: eigen3, Openmesh and MeshlabServer.
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
The dataset used in our paper can be downloaded here.
Or you can also construct your dataset that can be used in our code. To show how to construct a dataset that can be used in the code, we give a sample script that constructs a toy dataset that can construct the packed dataset. Firstly, build the code for ACAP interpolation (you should change the include/lib path in the [YourProjectPath]/data/sample_data/code_for_converting_seed_to_dataset/vertex2acap/CMakelists.txt):
cd [YourProjectPath]/data/sample_data/code_for_converting_seed_to_dataset/vertex2acap
python build_acap.py
Then, download some seed data into the [YourProjectPath]/data/sample_data/seed folder, and then convert the seed data into a packed dataset (you should change the meshlabserver path in [YourProjectPath]/data/sample_data/code_for_converting_seed_to_dataset/sample_points_for_one_mesh.py):
cd [YourProjectPath]/data/sample_data/code_for_converting_seed_to_dataset
python convert_seed_to_dataset.py
For simplicity, you can also directly download the constructed packed dataset into [YourProjectPath]/data/sample_data/code_for_converting_seed_to_dataset/packed_data.
Train with the dataset
To train with the constructed dataset:
cd [YourProjectPath]/model
python train_sample.py
The settings (the weights of the loss terms, the dataset, etc) of the training process can also be adjusted in the train_sample.py. The training results are saved in cd [YourProjectPath]/model/results.
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}
}