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Cross-Scale Cost Aggregation for Stereo Matching

Title
Cross-Scale Cost Aggregation for Stereo Matching
Authors
Zhang, KangFang, YuqiangMin, DongboSun, LifengYang, ShiqiangYan, Shuicheng
Ewha Authors
민동보
Issue Date
2017
Journal Title
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN
1051-8215JCR Link

1558-2205JCR Link
Citation
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY vol. 27, no. 5, pp. 965 - 976
Keywords
Cost aggregationlocal stereo matchingmultiscale
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Indexed
SCI; SCIE; SCOPUS WOS
Document Type
Article
Abstract
This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an interscale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets.
DOI
10.1109/TCSVT.2015.2513663
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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