Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-supervised depth completion nowadays faces other problems: moving objects, occluded/dark light/low texture parts, long-distance completion, and cross-modal fusion. In the face of these problems, the most critical novelty of this work lies in that the self-supervised mechanism is designed to train the depth completion network by MSC constraint. It not only makes better use of depth-temporal data, but also plays the advantage of photometric-temporal constraint. With the self-supervised mechanism of MSC constraint, the overall system outperforms many other self-supervised networks, even exceeding partially supervised networks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.