M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network

Understand

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.