Hierarchical Guided-Image-Filtering for Efficient Stereo Matching

Author:

Zhu ChengtaoORCID,Chang Yau-ZenORCID

Abstract

Stereo matching is complicated by the uneven distribution of textures on the image pairs. We address this problem by applying the edge-preserving guided-Image-filtering (GIF) at different resolutions. In contrast to most multi-scale stereo matching algorithms, parameters of the proposed hierarchical GIF model are in an innovative weighted-combination scheme to generate an improved matching cost volume. Our method draws its strength from exploiting texture in various resolution levels and performing an effective mixture of the derived parameters. This novel approach advances our recently proposed algorithm, the pervasive guided-image-filtering scheme, by equipping it with hierarchical filtering modules, leading to disparity images with more details. The approach ensures as many different-scale patterns as possible to be involved in the cost aggregation and hence improves matching accuracy. The experimental results show that the proposed scheme achieves the best matching accuracy when compared with six well-recognized cutting-edge algorithms using version 3 of the Middlebury stereo evaluation data sets.

Funder

The Ministry of Science and Technology, Taiwan

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference31 articles.

1. Stereo matching by training a convolutional neural network to compare image patches;Zbontar;J. Mach. Learn. Res.,2016

2. A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning

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