  We revisit the problem of visual depth estimation in the context of
autonomous vehicles. Despite the progress on monocular depth estimation in
recent years, we show that the gap between monocular and stereo depth accuracy
remains large$-$a particularly relevant result due to the prevalent reliance
upon monocular cameras by vehicles that are expected to be self-driving. We
argue that the challenges of removing this gap are significant, owing to
fundamental limitations of monocular vision. As a result, we focus our efforts
on depth estimation by stereo. We propose a novel semi-supervised learning
approach to training a deep stereo neural network, along with a novel
architecture containing a machine-learned argmax layer and a custom runtime
(that will be shared publicly) that enables a smaller version of our stereo DNN
to run on an embedded GPU. Competitive results are shown on the KITTI 2015
stereo dataset. We also evaluate the recent progress of stereo algorithms by
measuring the impact upon accuracy of various design criteria.
