Adaptive confidence thresholding for monocular depth estimation

TL;DR

This paper introduces an adaptive confidence thresholding method for monocular depth estimation that leverages pseudo ground truth from stereo matching, improving accuracy and boundary handling in self-supervised learning.

Contribution

It proposes a novel confidence threshold learning framework and a probabilistic refinement method using pixel-adaptive convolution, advancing self-supervised monocular depth estimation.

Findings

Outperforms state-of-the-art methods in accuracy

Effectively handles occlusion and object boundaries

Improves existing confidence estimation approaches

Abstract

Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results across object boundaries and is incapable of handling occlusion explicitly. In this paper, we propose a new approach to leverage pseudo ground truth depth maps of stereo images generated from self-supervised stereo matching methods. The confidence map of the pseudo ground truth depth map is estimated to mitigate performance degeneration by inaccurate pseudo depth maps. To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps. The pseudo depth labels filtered out by the thresholded confidence map are used to supervise the monocular depth network. Furthermore, we propose the probabilistic framework that refines the monocular depth map with the help of its uncertainty map through the pixel-adaptive convolution (PAC) layer. Experimental results demonstrate superior performance to state-of-the-art monocular depth estimation methods. Lastly, we exhibit that the proposed threshold learning can also be used to improve the performance of existing confidence estimation approaches.