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.
Highlights
Stereo vision aims at providing rich distance information of the captured scenes via image pairs.This is normally accomplished by matching algorithms to generate dense disparity maps
The stereo matching algorithm based on deep learning regards the process of deriving the disparity map as a classification problem or a regression problem
Inspired by the multiscale scheme of Zhang [29], we extend the pervasive guided-image-filtering, Pervasive Guided-Image-Filtering (PGIF) [25], to exploit the cross-scale features in the cost volume
Summary
Stereo vision aims at providing rich distance information of the captured scenes via image pairs. Disparity values are regressed for aggregation from the cost volume using 3D convolutions Another method to implement deep learning-based stereo matching is to use the networks to exploit context information. Tree filtering [15], domain transformation [16], recursive edge-aware filter [17], and full-image guided filtering [18] were proposed to decouple computational complexity with the support window size These approaches all suffer from the weight-decay problem when there is a significant intensity difference between neighboring pixels. We created an innovative aggregation approach that efficiently combines the model parameters of PGIF [25] to allow the features of the image pairs in different resolutions to be considered; The scheme is unique in its parameter-based aggregation, rather than the cost-volume-based approaches in the current literature, allowing efficient calculation with superior performance; The proposed scheme outperforms most of the state-of-art algorithms in terms of disparity accuracy even without the refinement procedure
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