Sparse Stereo Disparity Map Densification using Hierarchical Image Segmentation

Abstract : We describe a novel method for propagating disparity values using hierarchical segmentation by waterfall and robust regression models. High confidence disparity values obtained by state of the art stereo matching algorithms are interpolated using a coarse to fine approach. We start from a coarse segmentation of the image and try to fit each region’s disparities using robust regression models. If the fit is not satisfying, the process is repeated on a finer region’s segmentation. Erroneous values in the initial sparse disparity maps are generally excluded, as we use robust regressions algorithms and left-right consistency checks. Final disparity maps are therefore not only denser but can also be more accurate. The proposed method is general and independent from the sparse disparity map generation: it can therefore be used as a post-processing step for any stereo-matching algorithm.
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13th International Symposium, ISMM 2017, May 2017, Fontainebleau, France. pp.172-184, 2017, Mathematical Morphology and Its Applications to Signal and Image Processing. 〈http://cmm.ensmp.fr/ismm2017/〉
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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Partage selon les Conditions Initiales 4.0 International License

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Sébastien Drouyer, Serge Beucher, Michel Bilodeau, Maxime Moreaud, Loïc Sorbier. Sparse Stereo Disparity Map Densification using Hierarchical Image Segmentation. 13th International Symposium, ISMM 2017, May 2017, Fontainebleau, France. pp.172-184, 2017, Mathematical Morphology and Its Applications to Signal and Image Processing. 〈http://cmm.ensmp.fr/ismm2017/〉. 〈hal-01484143〉

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