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Remarkable results have been achieved by DCNN based self-supervised depthestimation approaches. However, most of these approaches can only handle eitherday-time or night-time images, while their performance degrades for all-dayimages due to large domain shift and the variation of illumination between dayand night images. To relieve these limitations, we propose a domain-separatednetwork for self-supervised depth estimation of all-day images. Specifically,to relieve the negative influence of disturbing terms (illumination, etc.), wepartition the information of day and night image pairs into two complementarysub-spaces: private and invariant domains, where the former contains the uniqueinformation (illumination, etc.) of day and night images and the lattercontains essential shared information (texture, etc.). Meanwhile, to guaranteethat the day and night images contain the same information, thedomain-separated network takes the day-time images and corresponding night-timeimages (generated by GAN) as input, and the private and invariant featureextractors are learned by orthogonality and similarity loss, where the domaingap can be alleviated, thus better depth maps can be expected. Meanwhile, thereconstruction and photometric losses are utilized to estimate complementaryinformation and depth maps effectively. Experimental results demonstrate thatour approach achieves state-of-the-art depth estimation results for all-dayimages on the challenging Oxford RobotCar dataset, proving the superiority ofour proposed approach.

Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation