Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space, both are unable to handle geometric inconsistency in the cost volume effectively. In this paper, we propose GoMVS to aggregate geometrically consistent costs, yielding better utilization of adjacent geometries. More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals. We achieve this by the geometric consistent propagation (GCP) module. It computes the correspondence from the adjacent depth hypothesis space to the reference depth space using surface normals, then uses the correspondence to propagate adjacent costs to the reference geometry, followed by a convolution for aggregation. Our method achieves new state-of-the-art performance on DTU, Tanks & Temple, and ETH3D datasets. Notably, our method ranks 1st on the Tanks & Temple Advanced benchmark.

翻译：在基于学习的多视图立体网络中，匹配代价聚合发挥着基础性作用。然而，由于局部几何不一致性，直接聚合相邻代价会导致次优结果。现有方法要么寻求选择性聚合，要么在二维空间中改进聚合深度，两者均无法有效处理代价体中的几何不一致性。本文提出GoMVS方法，通过聚合几何一致的代价，更充分地利用相邻几何信息。具体而言，我们利用局部几何平滑性与表面法向，将相邻代价对应并传播至参考像素。我们通过几何一致性传播模块实现这一目标：该模块利用表面法向计算相邻深度假设空间到参考深度空间的对应关系，随后利用该对应关系将相邻代价传播至参考几何，最后通过卷积完成聚合。我们的方法在DTU、Tanks & Temple和ETH3D数据集上取得了新的最优性能。值得注意的是，本方法在Tanks & Temple高级基准测试中排名第一。