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.

翻译：深度学习模型在这一领域达到最先进的业绩。一套用于估计单面深度的新办法包括将回归任务转化为分类任务。然而,对于社区递减分类方法缺乏详细的描述和比较,也没有深入探讨其不确定性估计潜力。为此,本文件将介绍CAR方法的分类和摘要、CAR的新的不确定性估计解决方案,以及一套关于基于CAR的KITTI数据集模型深度准确性和不确定性量化的实验。这些实验反映了CAR方法在两种主干线上的可移动性差异。与此同时,新提出的不确定性估计方法可以只用一种前向传播,超过组合方法。