  In deep multi-view stereo networks, cost regularization is crucial to achieve
accurate depth estimation. Since 3D cost volume filtering is usually
memory-consuming, recurrent 2D cost map regularization has recently become
popular and has shown great potential in reconstructing 3D models of different
scales. However, existing recurrent methods only model the local dependencies
in the depth domain, which greatly limits the capability of capturing the
global scene context along the depth dimension. To tackle this limitation, we
propose a novel non-local recurrent regularization network for multi-view
stereo, named NR2-Net. Specifically, we design a depth attention module to
capture non-local depth interactions within a sliding depth block. Then, the
global scene context between different blocks is modeled in a gated recurrent
manner. This way, the long-range dependencies along the depth dimension are
captured to facilitate the cost regularization. Moreover, we design a dynamic
depth map fusion strategy to improve the algorithm robustness. Our method
achieves state-of-the-art reconstruction results on both DTU and Tanks and
Temples datasets.
