Recently end-to-end unsupervised deep learning methods have achieved an
effect beyond geometric methods for visual depth and ego-motion estimation
tasks. These data-based learning methods perform more robustly and accurately
in some of the challenging scenes. The encoder-decoder network has been widely
used in the depth estimation and the RCNN has brought significant improvements
in the ego-motion estimation. Furthermore, the latest use of Generative
Adversarial Nets(GANs) in depth and ego-motion estimation has demonstrated that
the estimation could be further improved by generating pictures in the game
learning process. This paper proposes a novel unsupervised network system for
visual depth and ego-motion estimation: Stacked Generative Adversarial
Network(SGANVO). It consists of a stack of GAN layers, of which the lowest
layer estimates the depth and ego-motion while the higher layers estimate the
spatial features. It can also capture the temporal dynamic due to the use of a
recurrent representation across the layers. See Fig.1 for details. We select
the most commonly used KITTI [1] data set for evaluation. The evaluation
results show that our proposed method can produce better or comparable results
in depth and ego-motion estimation.

该研究提出了一种新的无监督网络系统用于视觉深度和自我运动估计：堆叠生成对抗网络 (SGANVO)。该系统由一堆 GAN 层组成，其中最底层估计深度和自我运动，而更高的层估计空间特征，并且由于在层之间使用了循环表示，可以捕捉时间动态。该方法在 KITTI 数据集上获得了更好或可比较的深度和自我运动估计结果。

SGANVO: 堆叠生成对抗网络进行无监督的深度视觉里程计与深度估计

SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with  Stacked Generative Adversarial Networks

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。