  Given stereo or egomotion image pairs, a popular and successful method for
unsupervised learning of monocular depth estimation is to measure the quality
of image reconstructions resulting from the learned depth predictions.
Continued research has improved the overall approach in recent years, yet the
common framework still suffers from several important limitations, particularly
when dealing with points occluded after transformation to a novel viewpoint.
While prior work has addressed this problem heuristically, this paper
introduces a z-buffering algorithm that correctly and efficiently handles
occluded points. Because our algorithm is implemented with operators typical of
machine learning libraries, it can be incorporated into any existing
unsupervised depth learning framework with automatic support for
differentiation. Additionally, because points having negative depth after
transformation often signify erroneously shallow depth predictions, we
introduce a loss function to penalize this undesirable behavior explicitly.
Experimental results on the KITTI data set show that the z-buffer and negative
depth loss both improve the performance of a state of the art depth-prediction
network.
