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Monocular depth is important in many tasks, such as 3D reconstruction andautonomous driving. Deep learning based models achieve state-of-the-artperformance in this field. A set of novel approaches for estimating monoculardepth consists of transforming the regression task into a classification one.However, there is a lack of detailed descriptions and comparisons forClassification Approaches for Regression (CAR) in the community and no in-depthexploration of their potential for uncertainty estimation. To this end, thispaper will introduce a taxonomy and summary of CAR approaches, a newuncertainty estimation solution for CAR, and a set of experiments on depthaccuracy and uncertainty quantification for CAR-based models on KITTI dataset.The experiments reflect the differences in the portability of various CARmethods on two backbones. Meanwhile, the newly proposed method for uncertaintyestimation can outperform the ensembling method with only one forwardpropagation.

On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression