  Recently unsupervised learning of depth from videos has made remarkable
progress and the results are comparable to fully supervised methods in outdoor
scenes like KITTI. However, there still exist great challenges when directly
applying this technology in indoor environments, e.g., large areas of
non-texture regions like white wall, more complex ego-motion of handheld
camera, transparent glasses and shiny objects. To overcome these problems, we
propose a new optical-flow based training paradigm which reduces the difficulty
of unsupervised learning by providing a clearer training target and handles the
non-texture regions. Our experimental evaluation demonstrates that the result
of our method is comparable to fully supervised methods on the NYU Depth V2
benchmark. To the best of our knowledge, this is the first quantitative result
of purely unsupervised learning method reported on indoor datasets.
