  Monocular depth is important in many tasks, such as 3D reconstruction and
autonomous driving. Deep learning based models achieve state-of-the-art
performance in this field. A set of novel approaches for estimating monocular
depth consists of transforming the regression task into a classification one.
However, there is a lack of detailed descriptions and comparisons for
Classification Approaches for Regression (CAR) in the community and no in-depth
exploration of their potential for uncertainty estimation. To this end, this
paper will introduce a taxonomy and summary of CAR approaches, a new
uncertainty estimation solution for CAR, and a set of experiments on depth
accuracy and uncertainty quantification for CAR-based models on KITTI dataset.
The experiments reflect the differences in the portability of various CAR
methods on two backbones. Meanwhile, the newly proposed method for uncertainty
estimation can outperform the ensembling method with only one forward
propagation.
