Abstract:
Learning-based multi-view stereo (MVS) has recently gained great popularity, which can efficiently infer depth map and reconstruct fine-grained scene geometry. Previous methods calculate the variance of the corresponding pixel pairs to determine whether they are matched mostly based on the pixel-wise measure, which fails to consider the interdependence among pixels and is ineffective on the matching of texture-less or occluded regions. These false matching problems challenge MVS and result in its most failure cases. To address the issues, we introduce a Long-range Attention Network (LANet) to selectively aggregate reference features to each position to capture the long-range interdependence across the entire space. As a result, similar features relate to each other regardless of their distance, propagating more guiding information for the effective match. Furthermore, we introduce a new loss to supervise the intermediate probability volume by constraining its distribution reasonably centered at the true depth. Extensive experiments on large-scale DTU dataset demonstrate that the proposed LANet achieves the new state-of-the-art performance, outperforming previous methods by a large margin. Our method is generic and also achieves comparable results on outdoor Tanks and Temples dataset without any fine-tuning, which validates our method's generalization ability.
