Night vision self-supervised Reflectance-Aware Depth Estimation based on reflectance

The depth estimation of nighttime images is a challenging problem due to the lack of accurate ground-truth depth labels. Although various self-supervised methods leveraging texture information have been proposed to solve the problem, the performance is still not satisfactory due to the imaging limitations of visible cameras. To this end, we propose a self-supervised Reflectance-Aware Depth Estimation approach based on reflectance for nighttime images. Two major factors strengthen the proposed approach: a Reflectance Extraction Network and a feature consistency loss. We introduce the Reflectance Extraction Network to extract texture information based on the finding that the texture is beneficial for depth estimation. Then, we utilize the feature consistency loss to help the baseline network to learn the intrinsic feature rather than the images' light. Experiment results on the challenging Oxford RobotCar dataset confirm the robustness and effectiveness of our approach.