  We propose an efficient multi-view stereo (MVS) network for infering depth
value from multiple RGB images. Recent studies have shown that mapping the
geometric relationship in real space to neural network is an essential topic of
the MVS problem. Specifically, these methods focus on how to express the
correspondence between different views by constructing a nice cost volume. In
this paper, we propose a more complete cost volume construction approach based
on absorbing previous experience. First of all, we introduce the self-attention
mechanism to fully aggregate the dominant information from input images and
accurately model the long-range dependency, so as to selectively aggregate
reference features. Secondly, we introduce the group-wise correlation to
feature aggregation, which greatly reduces the memory and calculation burden.
Meanwhile, this method enhances the information interaction between different
feature channels. With this approach, a more lightweight and efficient cost
volume is constructed. Finally we follow the coarse to fine strategy and refine
the depth sampling range scale by scale with the help of uncertainty
estimation. We further combine the previous steps to get the attention thin
volume. Quantitative and qualitative experiments are presented to demonstrate
the performance of our model.
