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.

本文提出了一种使用自监督立体匹配方法生成的伪真实深度地图来辅助单目深度估计的新方法，并利用门限网络调整伪深度图。通过基于像素自适应卷积层提高单目深度估计精度，并取得了优异的实验结果。