Stereo Matching Method Based on Multiscale Attention Network

doi:10.13190/j.jbupt.2020-204

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 27-34.doi: 10.13190/j.jbupt.2020-204

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Stereo Matching Method Based on Multiscale Attention Network

BIAN Ji-long¹, WANG Hou-bo¹, LI Jin-feng²

1. College of Information & Computer Engineering, Northeast Forestry University, Harbin 150040, China;
2. College of Computer & Information Technology, Mudanjiang Normal University, Mudanjiang 157011, China

Received:2020-10-19 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: Aiming at the problem that the depth of network is related to the size of training image patches and improving the matching accuracy for the weak texture and edge regions, a multiscale attention network for stereo matching is presented. The method is divided into two stages:in the first stage, a deep network for computing matching cost is proposed, which is composed of basic layer and scale layer, in the second stage, a disparity refinement network based on multiscale attention is proposed, in which multiple disparity clues are combined and multiscale attention is added to boost stereo matching accuracy. The percentage of 3-pixel bad points on KITTI 2012, KITTI 2015, and SceneFlow is 1.13%, 1.87%, and 2.29%, respectively. Experiments show that compared with the current domestic and foreign advanced methods, a stereo matching method based on multiscale attention network made a great improvement in matching accuracy, especially better improvement for the weak texture and edge regions.

Key words: deep network, stereo matching, matching cost, multiscale attention, disparity refinement

CLC Number:

TP391.41

BIAN Ji-long, WANG Hou-bo, LI Jin-feng. Stereo Matching Method Based on Multiscale Attention Network[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 27-34.

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