Vis-MVSNet: Visibility-Aware Multi-view Stereo Network

Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration, resulting in erroneous cost aggregation from occluded pixels. In this paper, we explicitly infer and integrate the pixel-wise occlusion information in the MVS network via the matching uncertainty estimation. The pair-wise uncertainty map is jointly inferred with the pair-wise depth map, which is further used as weighting guidance during the multi-view cost volume fusion. As such, the adverse influence of occluded pixels is suppressed in the cost fusion. The proposed framework Vis-MVSNet significantly improves depth accuracy in reconstruction scenes with severe occlusion. Extensive experiments are performed on DTU , BlendedMVS , Tanks and Temples and ETH3D datasets to justify the effectiveness of the proposed framework.