Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021).

Overview

AA-RMVSNet

Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021) in PyTorch.

paper link: arXiv | CVF

Change Log

  • Jun 17, 2021: Initialize repo
  • Jun 27, 2021: Update code
  • Aug 10, 2021: Update paper link
  • Oct 14, 2021: Update bibtex

Data Preparation

How to run

  1. 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
  2. Set root of datasets as env variables in env.sh.
  3. Train AA-RMVSNet on DTU dataset (note that training requires a large amount of GPU memory):
    ./scripts/train_dtu.sh
  4. Predict depth maps and fuse them to get point clouds of DTU:
    ./scripts/eval_dtu.sh
    ./scripts/fusion_dtu.sh
  5. Predict depth maps and fuse them to get point clouds of Tanks and Temples:
    ./scripts/eval_tnt.sh
    ./scripts/fusion_tnt.sh

Note: if permission issues are encountered, try 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.

Owner
Qingtian Zhu
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