Increasing Efficiency in Disparity Calculation

Ralli, Jarno; Pelayo, Francisco J.; Díaz, Javier

doi:10.1007/978-3-540-75555-5_28

Cited by 3 publications

(1 citation statement)

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“…Apart from diffusion [23] and interpolation [18], a few general propagation methods exist in the literature. From a disparity map where only edges are matched, VMP [17] diffuses disparity values by using predefined masks and a voting process.…”

Section: Introductionmentioning

confidence: 99%

Sparse Stereo Disparity Map Densification Using Hierarchical Image Segmentation

Drouyer

Beucher

Bilodeau

et al. 2017

Lecture Notes in Computer Science

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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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Section: Introductionmentioning

confidence: 99%