Open AccessCSNet: Cascade stereo matching network using multi‐information cost volume
Author(s) -
Shao XiaoTao,
Zhang Wen,
Guo MingKun,
Guo SiQi,
Qian ManYi
Publication year - 2021
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12050
Subject(s) - artificial intelligence , concatenation (mathematics) , volume (thermodynamics) , computer science , matching (statistics) , pattern recognition (psychology) , computer vision , scale (ratio) , cascade , feature (linguistics) , mathematics , geography , engineering , statistics , linguistics , physics , philosophy , cartography , quantum mechanics , combinatorics , chemical engineering
The disparity map produced by matching a pair of rectified stereo images provides estimated depth information and accurate distance calculations for autonomous driving. For most stereo matching networks, cost volume plays a crucial role in the accuracy of disparity maps. To increase the accuracy of disparity maps, an improved cost volume method called multi‐information cost volume (MICV) is proposed, with the fusion of concatenation volume and improved correlation volume, which is calculated both in inner product space and Euclidean space, to measure the correlation similarity between features in the left and right images. The inclusion of a squeeze and excitation (SE) module further improves MICV by adjusting the contribution of the correlation volume. To refine the disparity map and enhance the semantics of small objects, a cascade stereo network called CSNet is proposed, with a dilation feature fusion unit (DFFU) to calculate and integrate disparity maps from three different scale branches. The smaller scale branches are gradually integrated into the larger scale branches to implement the transmission of semantic information. The proposed method was evaluated using several established benchmarks including the Sceneow, KITTI2012 and KITTI2015 datasets. Experimental results demonstrate that authors' method produces more accurate disparity maps compared with existing state‐of‐the‐art methods.