Self-Supervised Monocular Depth Estimation via Effective Local Consistency and Multi-scale Attention

Abstract

Recent studies have shown that pixel-level depth estimators can be learned through the self-supervised method, resulting in progress in depth prediction. However, due to the complexity of realistic scenarios, the assumption of brightness constancy, which is the basis of the optimization function designed by existing methods, is invalid in practice. As a result, the optimization target may be chosen in an inefficient manner, and thus affecting the performance of depth estimation. This paper tackles this issue by integrating local consistency into the framework. Specially, we exploit the gradient from depth map to generate a valid mask, which is capable of locating the key region and optimizing the photometric loss by ensuring the local consistency of masked points between target image and synthesis image. In addition, we combine the depth maps from multiple scales to effectively exploit multi-scale features and excavate detailed information of depth map. Comprehensive evaluation results on the KITTI dataset demonstrate that the proposed algorithm greatly improves the ability of capturing details of depth changes and achieves more accurate results.