Adaptive Weighted Network With Edge Enhancement Module For Monocular Self-Supervised Depth Estimation

Abstract

Monocular self-supervised depth estimation can be easily applied in many areas since only a single camera is required. However, current methods do not predict well in depth borders. Besides, factors such as occlusion and texture sparsity can lead to the failure of the photometric consistency, affecting the prediction performance. To overcome these deficiencies, an adaptive weighted monocular self-supervised depth estimation framework that exploits enhanced edge information and texture sparsity based adaptive weights is proposed. In particular, a module named edge enhancement module (EEM) is designed to be embedded into the current depth prediction network to extract edge details for clearer depth prediction in depth borders. Moreover, a texture sparsity based adaptive weighted (TSAW) loss is introduced to as-sign different weights according to texture sparsity, enabling a more targeted construction of geometric constraints. Experimental results on the KITTI dataset demonstrate that the proposed network outperforms state-of-the-art methods.