  Consumer-level depth cameras and depth sensors embedded in mobile devices
enable numerous applications, such as AR games and face identification.
However, the quality of the captured depth is sometimes insufficient for 3D
reconstruction, tracking and other computer vision tasks. In this paper, we
propose a self-supervised depth denoising approach to denoise and refine depth
coming from a low quality sensor. We record simultaneous RGB-D sequences with
unzynchronized lower- and higher-quality cameras and solve a challenging
problem of aligning sequences both temporally and spatially. We then learn a
deep neural network to denoise the lower-quality depth using the matched
higher-quality data as a source of supervision signal. We experimentally
validate our method against state-of-the-art filtering-based and deep denoising
techniques and show its application for 3D object reconstruction tasks where
our approach leads to more detailed fused surfaces and better tracking.
