Self-supervised depth estimation method and system

Abstract

Depth perception has become of increased interest in the image community due to the increasing usage of deep neural networks for the generation of dense depth maps. The applications of depth perception estimation, however, may still be limited due to the needs of a large amount of dense ground-truth depth for training. It is contemplated that a self-supervised control strategy may be developed for estimating depth maps using color images and data provided by a sensor system (e.g., sparse LiDAR data). Such a self-supervised control strategy may leverage superpixels (i.e., group of pixels that share common characteristics, for instance, pixel intensity) as local planar regions to regularize surface normal derivatives from estimated depth together with the photometric loss. The control strategy may be operable to produce a dense depth map that does not require a dense ground-truth supervision.