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.

在自动驾驶车辆的背景下，本文重新审视视觉深度估计问题。我们提出了一种新颖的半监督学习方法来训练深度立体神经网络，并提出了一种包含机器学习的 argmax 层和定制运行时的体系结构，可在嵌入式 GPU 上运行一个较小版本的我们的 Stereo DNN。在 KITTI 2015 立体数据集上展示了有竞争力的结果。