Authors:Mircea Paul Muresan;Sergiu Nedevschi andRadu Danescu
Affiliation:Technical University of Cluj-Napoca, Romania
Keyword(s):Dense Stereo, Block Matching, Slanted Surfaces, Disparity Refinement, Binary Descriptors.
RelatedOntology Subjects/Areas/Topics:Applications ;Computer Vision, Visualization and Computer Graphics ;Geometry and Modeling ;Image-Based Modeling ;Motion, Tracking and Stereo Vision ;Pattern Recognition ;Software Engineering ;Stereo Vision and Structure from Motion
Abstract:Stereo cameras are a suitable solution for reconstructing the 3D information of the observed scenes, and,because of their low price and ease to set up and operate, they can be used in a wide area of applications,ranging from autonomous driving to advanced driver assistance systems or robotics. Due to the high qualityof the results, energy based reconstruction methods like semi global matching have gained a lot of popularityin recent years. The disadvantages of semi global matching are the large memory footprint and the highcomputational complexity. In contrast, window based matching methods have a lower complexity, and areleaner with respect to the memory consumption. The downside of block matching methods is that they aremore error prone, especially on surfaces which are not parallel to the image plane. In this paper we present anovel block matching scheme that improves the quality of local stereo correspondence algorithms. The firstcontribution of the paper consists in anoriginal method for reliably reconstructing the environment on slantedsurfaces. The second contribution consists in the creation of set of local constraints that filter out possibleoutlier disparity values. The third and final contribution consists in the creation of a refinement techniquewhich improves the resulted disparity map. The proposed stereo correspondence approach has been validatedon the KITTI stereo dataset.(More)
Stereo cameras are a suitable solution for reconstructing the 3D information of the observed scenes, and,
because of their low price and ease to set up and operate, they can be used in a wide area of applications,
ranging from autonomous driving to advanced driver assistance systems or robotics. Due to the high quality
of the results, energy based reconstruction methods like semi global matching have gained a lot of popularity
in recent years. The disadvantages of semi global matching are the large memory footprint and the high
computational complexity. In contrast, window based matching methods have a lower complexity, and are
leaner with respect to the memory consumption. The downside of block matching methods is that they are
more error prone, especially on surfaces which are not parallel to the image plane. In this paper we present a
novel block matching scheme that improves the quality of local stereo correspondence algorithms. The first
contribution of the paper consists in an original method for reliably reconstructing the environment on slanted
surfaces. The second contribution consists in the creation of set of local constraints that filter out possible
outlier disparity values. The third and final contribution consists in the creation of a refinement technique
which improves the resulted disparity map. The proposed stereo correspondence approach has been validated
on the KITTI stereo dataset.