  We present an efficient multi-view stereo (MVS) network for 3D reconstruction
from multiview images. While previous learning based reconstruction approaches
performed quite well, most of them estimate depth maps at a fixed resolution
using plane sweep volumes with a fixed depth hypothesis at each plane, which
requires densely sampled planes for desired accuracy and therefore is difficult
to achieve high resolution depth maps. In this paper we introduce a
coarseto-fine depth inference strategy to achieve high resolution depth. This
strategy estimates the depth map at coarsest level, while the depth maps at
finer levels are considered as the upsampled depth map from previous level with
pixel-wise depth residual. Thus, we narrow the depth searching range with
priori information from previous level and construct new cost volumes from the
pixel-wise depth residual to perform depth map refinement. Then the final depth
map could be achieved iteratively since all the parameters are shared between
different levels. At each level, the self-attention layer is introduced to the
feature extraction block for capturing the long range dependencies for depth
inference task, and the cost volume is generated using similarity measurement
instead of the variance based methods used in previous work. Experiments were
conducted on both the DTU benchmark dataset and recently released BlendedMVS
dataset. The results demonstrated that our model could outperform most
state-of-the-arts (SOTA) methods. The codebase of this project is at
https://github.com/ArthasMil/AACVP-MVSNet.
