M³VSNet

The code is available now!!!

About

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M³VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences. Besides, we also incorporate the normal-depth consistency in the 3D point cloud format to improve the accuracy and continuity of the estimated depth maps. Experimental results show that M³VSNet establishes the state-of-the-arts unsupervised method and achieves comparable performance with previous supervised MVSNet on the DTU dataset and demonstrates the powerful generalization ability on the Tanks and Temples benchmark with effective improvement.

Please cite:

@article{Huang2020M3VSNet,
  title={M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network},
  author={Baichuan Huang and Hongwei Yi and Can Huang and Yijia He and Jingbin Liu and Xin Liu},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.09722v2}
}

How to use

Environment

python 3.6.9
pytorch 1.0.1
CUDA 10.1 cudnn 7.5.0

The conda environment is listed in requirements.txt

Train

Download the preprocessed DTU training data (Fixed training cameras, from Original MVSNet，or the Baiduyun link, the password is mo8w ), and upzip it as the MVS_TRANING folder
in train.sh, set MVS_TRAINING as your training data path
create a logdir called checkpoints
Train MVSNet: ./train.sh

Eval

Download the preprocessed test data DTU testing data (from Original MVSNet, or the Baiduyun link, the password is mo8w ) and unzip it as the DTU_TESTING folder, which should contain one cams folder, one images folder and one pair.txt file.
in test.sh, set DTU_TESTING as your testing data path and CKPT_FILE as your checkpoint file. You can find some models in the /checkpoint/. You can use the trained models to test your image.
Test MVSNet: ./test.sh

Results

	Acc.	Comp.	Overall.
MVSNet(D=196)	0.444	0.741	0.592
Unsup_MVS	0.881	1.073	0.977
MVS2	0.760	0.515	0.637
M3VSNet(D=192)	0.636	0.531	0.583

T&T Benchmark

The best unsupervised MVS network until April 17, 2020. See the leaderboard .

Acknowledgement

Thanks for the funding from Megvii Technology Limited. We acknowledge the following repositories MVSNet and MVSNet_pytorch. For more information about MVSNet series, please see the material.

glwhu/M3VSNet

M3VSNet