SABV-Depth: A biologically inspired deep learning network for monocular depth estimation

Abstract

Monocular depth estimation makes it possible for machines to perceive the real world. The prediction performance of the depth estimation network based on deep learning will be affected due to the depth of the deep network and the locality of convolution operations. The imitation of the biological visual system and its functional structure is becoming a research hotspot. In this paper, we study the interpretability relationship between the biological visual system and the monocular depth estimation network. By concretizing the attention mechanism in biological vision, we propose a monocular depth estimation network based on the self-attention mechanism, named SABV-Depth, which can improve prediction accuracy. Inspired by the biological visual interaction mechanism, we focus on the information transfer between each module of the network and improve the information retention ability, and enable the network to output a depth map with rich object information and detailed information. Further, a decoder module with an inner-connection is proposed to recover depth maps with sharp edge contours. Our method is experimentally validated on the KITTI dataset and NYU Depth V2 dataset. The results show that compared with other works, the proposed method improves prediction accuracy. Meanwhile, the depth map has more object information and detail information, and a better edge information processing effect.