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MVSTER

MVSTER: Epipolar Transformer for Efficient Multi-View Stereo, ECCV 2022. arXiv

This repository contains the official implementation of the paper: "MVSTER: Epipolar Transformer for Efficient Multi-View Stereo".

Introduction

MVSTER is a learning-based MVS method which achieves competitive reconstruction performance with significantly higher efficiency. MVSTER leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently. Specifically, the epipolar Transformer utilizes a detachable monocular depth estimator to enhance 2D semantics and uses cross-attention to construct data-dependent 3D associations along epipolar line. Additionally, MVSTER is built in a cascade structure, where entropy-regularized optimal transport is leveraged to propagate finer depth estimations in each stage.

Installation

MVSTER is tested on:

python 3.7
CUDA 11.1

Requirements

pip install -r requirements.txt

Training

Dowload DTU dataset. For convenience, can download the preprocessed DTU training data and Depths_raw (both from Original MVSNet), and upzip it as the $DTU_TRAINING folder. For training and testing with raw image size, you can download Rectified_raw, and unzip it.

├── Cameras    
├── Depths
├── Depths_raw   
├── Rectified
├── Rectified_raw (Optional)

In scripts/train_dtu.sh, set DTU_TRAINING as $DTU_TRAINING

Train MVSTER (Multi-GPU training):

Train with middle size (512x640):

bash ./scripts/train_dtu.sh mid exp_name

Train with raw size (1200x1600):

bash ./scripts/train_dtu.sh raw exp_name

After training, you will get model checkpoints in ./checkpoints/dtu/exp_name.

Testing

Download the preprocessed test data DTU testing data (from Original MVSNet) and unzip it as the $DTU_TESTPATH folder, which should contain one cams folder, one images folder and one pair.txt file.
In scripts/test_dtu.sh, set DTU_TESTPATH as $DTU_TESTPATH.
The DTU_CKPT_FILE is automatically set as your pretrained checkpoint file, you also can download my pretrained model.
Test with middle size:

bash ./scripts/test_dtu.sh mid exp_name

Test with raw size:

bash ./scripts/test_dtu.sh raw exp_name

Test with provided pretrained model:

bash scripts/test_dtu.sh mid benchmark --loadckpt PATH_TO_CKPT_FILE

After testing, you will get reconstructed point clouds of DTU test set in ./outputs/dtu/exp_name.

Metric

For quantitative evaluation, download SampleSet and Points from DTU's website. Unzip them and place Points folder in SampleSet/MVS Data/. The structure looks like:

SampleSet
├──MVS Data
      └──Points

For convinience evaluation, please install matlab (tested on Ubuntu 18.04) and uncomment mrun_rst function at the end of ./test_mvs4.py, and you also need to change the path of matlab excutable file (for me, it is /mnt/cfs/algorithm/xiaofeng.wang/jeff/code/MVS/misc/matlab/bin/matlab). Then you can evaluate point cloud reconstruction results when testing is finished.
You can also evaluate the metrics with the traditional steps: In evaluations/dtu/BaseEvalMain_web.m, set dataPath as the path to SampleSet/MVS Data/, plyPath as directory that stores the reconstructed point clouds and resultsPath as directory to store the evaluation results. Then run evaluations/dtu/BaseEvalMain_web.m in matlab.

Results on DTU (single RTX 3090)

	Acc.	Comp.	Overall.	Inf. Time
MVSTER (mid size)	0.350	0.276	0.313	0.09s
MVSTER (raw size)	0.340	0.266	0.303	0.17s

Point cloud results on DTU, Tanks and Temples, ETH3D

If you find this project useful for your research, please cite:

@misc{wang2022mvster,
      title={MVSTER: Epipolar Transformer for Efficient Multi-View Stereo}, 
      author={Xiaofeng Wang, Zheng Zhu, Fangbo Qin, Yun Ye, Guan Huang, Xu Chi, Yijia He and Xingang Wang},
      journal={arXiv preprint arXiv:2204.07346},
      year={2022}
}

Acknowledgements

Our work is partially baed on these opening source work: MVSNet, MVSNet-pytorch, cascade-stereo, PatchmatchNet.

We appreciate their contributions to the MVS community.