  Remarkable results have been achieved by DCNN based self-supervised depth
estimation approaches. However, most of these approaches can only handle either
day-time or night-time images, while their performance degrades for all-day
images due to large domain shift and the variation of illumination between day
and night images. To relieve these limitations, we propose a domain-separated
network for self-supervised depth estimation of all-day images. Specifically,
to relieve the negative influence of disturbing terms (illumination, etc.), we
partition the information of day and night image pairs into two complementary
sub-spaces: private and invariant domains, where the former contains the unique
information (illumination, etc.) of day and night images and the latter
contains essential shared information (texture, etc.). Meanwhile, to guarantee
that the day and night images contain the same information, the
domain-separated network takes the day-time images and corresponding night-time
images (generated by GAN) as input, and the private and invariant feature
extractors are learned by orthogonality and similarity loss, where the domain
gap can be alleviated, thus better depth maps can be expected. Meanwhile, the
reconstruction and photometric losses are utilized to estimate complementary
information and depth maps effectively. Experimental results demonstrate that
our approach achieves state-of-the-art depth estimation results for all-day
images on the challenging Oxford RobotCar dataset, proving the superiority of
our proposed approach.
