  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 M^3VSNet, 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. Our code is available at https://github.com/whubaichuan/M3VSNet.
