论文标题
关于使用分类方法进行回归的单眼深度估计和不确定性定量
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
论文作者
论文摘要
单眼深度在许多任务中很重要，例如3D重建和自动驾驶。基于深度学习的模型在该领域实现了最新的性能。一组估计单眼深度的新方法包括将回归任务转换为分类。但是，缺乏针对社区回归（CAR）的分类方法的详细描述和比较，并且没有深入探索其不确定性估计的潜力。为此，本文将介绍汽车方法的分类学和摘要，对汽车的新不确定性估计解决方案以及对Kitti数据集中基于汽车模型的深度准确性和不确定性量化的一组实验。实验反映了两个骨干上各种CAR方法的可移植性的差异。同时，新提出的不确定性估计方法只能以一个正向传播的速度优于结合方法。
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.
