Awesome
AA-RMVSNet
Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021) in PyTorch.
<img src="doc/arch.png" width="800"> <img src="doc/tnt.png" width="800">Change Log
- Jun 17, 2021: Initialize repo
- Jun 27, 2021: Update code
- Aug 10, 2021: Update paper link
- Oct 14, 2021: Update bibtex
- May 23, 2022: Update network architecture & pretrained model
Data Preparation
- Download the preprocessed DTU training data (also available at BaiduYun, PW: s2v2).
- For other datasets, please follow the practice in Yao Yao's MVSNet repo.
- Note that the newly released pretrained models are not compatible with the old codebase. Please update the code as well.
How to run
- Install required dependencies:
conda create -n drmvsnet python=3.6 conda activate drmvsnet conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch conda install -c conda-forge py-opencv plyfile tensorboardx
- Set root of datasets as env variables in
env.sh
. - Train AA-RMVSNet on DTU dataset (note that training requires a large amount of GPU memory):
./scripts/train_dtu.sh
- Predict depth maps and fuse them to get point clouds of DTU:
./scripts/eval_dtu.sh ./scripts/fusion_dtu.sh
- Predict depth maps and fuse them to get point clouds of Tanks and Temples:
Note: if permission issues are encountered, try./scripts/eval_tnt.sh ./scripts/fusion_tnt.sh
chmod +x <script_filename>
to allow execution.
Citation
@inproceedings{wei2021aa,
title={AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network},
author={Wei, Zizhuang and Zhu, Qingtian and Min, Chen and Chen, Yisong and Wang, Guoping},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={6187--6196},
year={2021}
}
Acknowledgements
This repository is heavily based on Xiaoyang Guo's PyTorch implementation.