Sparse Stereo Disparity Map Densification using Hierarchical Image Segmentation

Autor: Sébastien Drouyer, Serge Beucher, Michel Bilodeau, Maxime Moreaud, Loïc Sorbier
Přispěvatelé: Centre de Morphologie Mathématique (CMM), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), IFP Energies nouvelles (IFPEN)
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: 13th International Symposium, ISMM 2017
13th International Symposium, ISMM 2017, May 2017, Fontainebleau, France. pp.172-184
HAL
Popis: International audience; 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.
Databáze: OpenAIRE
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