Unsupervised Monocular Depth Estimation Based on Dual Attention Mechanism and Depth-Aware Loss

Abstract

Most existing monocular depth estimation approaches are su- pervised, but enough quantities of ground truth depth data are required during training. To cope with this, recent techniques deal with the depth estimation task in an unsupervised man- ner, i.e., replacing the use of depth data with easily obtained stereo images for training. Based on this, we propose a nov- el unsupervised learning architecture, which integrates dual attention mechanism into the framework and designs a depth- aware loss for better depth estimation. Specifically, to en- hance the ability of feature representations, we introduce a d- ual attention module to capture global feature dependencies in spatial and channel dimensions for scene understanding and depth estimation. Meanwhile, we propose a depth-aware loss that fully addresses the occlusion problem in brightness con- stancy assumption, the intrinsic characteristics of depth map, and the left-right consistency problem, respectively. Besides, an adversarial loss is employed to discriminate synthetic or realistic depth maps by training a discriminator so as to pro- duce better results. Extensive experiments on KITTI dataset show that our approach achieves state-of-the-art performance compared with other monocular depth estimation methods.