  Monocular depth estimation is the base task in computer vision. It has a
tremendous development in the decade with the development of deep learning. But
the boundary blur of the depth map is still a serious problem. Research finds
the boundary blur problem is mainly caused by two factors, first, the low-level
features containing boundary and structure information may loss in deeper
networks during the convolution process., second, the model ignores the errors
introduced by the boundary area due to the few portions of the boundary in the
whole areas during the backpropagation. In order to mitigate the boundary blur
problem, we focus on the above two impact factors. Firstly, we design a scene
understanding module to learn the global information with low- and high-level
features, and then to transform the global information to different scales with
our proposed scale transform module according to the different phases in the
decoder. Secondly, we propose a boundary-aware depth loss function to pay
attention to the effects of the boundary's depth value. The extensive
experiments show that our method can predict the depth maps with clearer
boundaries, and the performance of the depth accuracy base on NYU-depth v2 and
SUN RGB-D is competitive.
