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

• URL: http://arxiv.org/abs/2202.12369v1
• Date: Thu, 24 Feb 2022 21:40:51 GMT
• Title: On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
• Authors: Xuanlong Yu, Gianni Franchi, Emanuel Aldea
• Abstract summary: This paper introduces a taxonomy and summary of Classification Approaches for Regression approaches. It also introduces a new uncertainty estimation solution for CAR. Experiments reflect the differences in the portability of various CAR methods on two backbones.
• Score: 2.784501414201992
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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