  Monocular depth estimation is a fundamental task in computer vision and has
drawn increasing attention. Recently, some methods reformulate it as a
classification-regression task to boost the model performance, where continuous
depth is estimated via a linear combination of predicted probability
distributions and discrete bins. In this paper, we present a novel framework
called BinsFormer, tailored for the classification-regression-based depth
estimation. It mainly focuses on two crucial components in the specific task:
1) proper generation of adaptive bins and 2) sufficient interaction between
probability distribution and bins predictions. To specify, we employ the
Transformer decoder to generate bins, novelly viewing it as a direct set-to-set
prediction problem. We further integrate a multi-scale decoder structure to
achieve a comprehensive understanding of spatial geometry information and
estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene
understanding query is proposed to improve the estimation accuracy, which turns
out that models can implicitly learn useful information from an auxiliary
environment classification task. Extensive experiments on the KITTI, NYU, and
SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art
monocular depth estimation methods with prominent margins. Code and pretrained
models will be made publicly available at
\url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}.
