A fast stereo matching network with multi-cross attention

doi:10.3390/s21186016

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	A fast stereo matching network with multi-cross attention
	M. Wei; M. Zhu; Y. Wu; J. Sun; J. Wang and C. Liu
刊名	Sensors
	2021
卷号	21 期号:18
ISSN号	14248220
DOI	10.3390/s21186016
英文摘要	Stereo matching networks based on deep learning are widely developed and can obtain excellent disparity estimation. We present a new end-to-end fast deep learning stereo matching network in this work that aims to determine the corresponding disparity from two stereo image pairs. We extract the characteristics of the low-resolution feature images using the stacked hourglass structure feature extractor and build a multi-level detailed cost volume. We also use the edge of the left image to guide disparity optimization and sub-sample with the low-resolution data, ensuring excellent accuracy and speed at the same time. Furthermore, we design a multi-cross attention model for binocular stereo matching to improve the matching accuracy and achieve end-to-end disparity regression effectively. We evaluate our network on Scene Flow, KITTI2012, and KITTI2015 datasets, and the experimental results show that the speed and accuracy of our method are excellent. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/65245]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	M. Wei,M. Zhu,Y. Wu,et al. A fast stereo matching network with multi-cross attention[J]. Sensors,2021,21(18).
APA	M. Wei,M. Zhu,Y. Wu,J. Sun,&J. Wang and C. Liu.(2021).A fast stereo matching network with multi-cross attention.Sensors,21(18).
MLA	M. Wei,et al."A fast stereo matching network with multi-cross attention".Sensors 21.18(2021).