Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving. (arXiv:2205.08780v3 [cs.CV] UPDATED)

Although existing monocular depth estimation methods have made great
progress, predicting an accurate absolute depth map from a single image is
still challenging due to the limited modeling capacity of networks and the
scale ambiguity issue. In this paper, we introduce a fully Visual
Attention-based Depth (VADepth) network, where spatial attention and channel
attention are applied to all stages. By continuously extracting the
dependencies of features along the spatial and channel dimensions over a long
distance, VADepth network can effectively …

arxiv attention autonomous autonomous driving driving visual attention