  Deep learning-based multi-view stereo has emerged as a powerful paradigm for
reconstructing the complete geometrically-detailed objects from multi-views.
Most of the existing approaches only estimate the pixel-wise depth value by
minimizing the gap between the predicted point and the intersection of ray and
surface, which usually ignore the surface topology. It is essential to the
textureless regions and surface boundary that cannot be properly reconstructed.
To address this issue, we suggest to take advantage of point-to-surface
distance so that the model is able to perceive a wider range of surfaces. To
this end, we predict the distance volume from cost volume to estimate the
signed distance of points around the surface. Our proposed RA-MVSNet is
patch-awared, since the perception range is enhanced by associating
hypothetical planes with a patch of surface. Therefore, it could increase the
completion of textureless regions and reduce the outliers at the boundary.
Moreover, the mesh topologies with fine details can be generated by the
introduced distance volume. Comparing to the conventional deep learning-based
multi-view stereo methods, our proposed RA-MVSNet approach obtains more
complete reconstruction results by taking advantage of signed distance
supervision. The experiments on both the DTU and Tanks \& Temples datasets
demonstrate that our proposed approach achieves the state-of-the-art results.
