论文标题
可见性 - 可见的多视图立体网络
Visibility-aware Multi-view Stereo Network
论文作者
论文摘要
基于学习的多视图立体声（MVS）方法已证明了有希望的结果。但是，很少有现有网络明确考虑了像素的可见度，从而导致遮挡像素的错误成本聚集。在本文中，我们通过匹配的不确定性估计明确推断和集成了MVS网络中的像素遮挡信息。配对不确定性图是通过成对深度图共同推断出的，该图在多视图成本量融合过程中进一步用作加权指导。因此，成本融合会抑制遮挡像素的不利影响。提出的Vis-Mvsnet框架可显着提高严重阻塞的场景深度精度。在DTU，BlendenDMV，Tank和Temples数据集上进行了广泛的实验，以证明所提出的框架的有效性。
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 accuracies in the scenes with severe occlusion. Extensive experiments are performed on DTU, BlendedMVS, and Tanks and Temples datasets to justify the effectiveness of the proposed framework.
