Parallax attention stereo matching network based on the improved group-wise correlation stereo network.

ABSTRACT: Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous driving and depth sensing. However, state-of-the-art stereo algorithms, even with the deep neural network framework, still have difficulties at finding correct correspondences in near-range regions and object edge cues. To reinforce the precision of disparity prediction, in the present study, we propose a parallax attention stereo matching algorithm based on the improved group-wise correlation stereo network to learn the disparity content from a stereo correspondence, and it supports end-to-end predictions of both disparity map and edge map. Particular, we advocate for a parallax attention module in three-dimensional (disparity, height and wi

SUBMITTER: Yu X 

PROVIDER: S-EPMC8827418 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature