  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.
