  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.
