Abstract
Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.
Highlights
Stereo matching is the problem of recovering corresponding points from different image views, and is one of the indispensable ingredients for 3D exploitation of imagery in the fields of Photogrammetry and Computer vision
A reasonable penalty estimation method as well as an image edge constraint are imposed on the stereo matching procedure to enhance the matching performance, and the edge disparity results derived from the two matching process are weighted for better estimation in depth discontinuity areas
This study presents a convenient way to estimate proper penalty parameters for the semi-global matching (SGM) cost aggregation
Summary
Stereo matching is the problem of recovering corresponding points from different image views, and is one of the indispensable ingredients for 3D exploitation of imagery in the fields of Photogrammetry and Computer vision. The smoothness term assigns a large penalty to those neighboring pixels conveying different disparity values, the similar pixels can be merged Various optimization techniques, such as graph cuts (Boykov et al, 2001; Wang et al, 2013), belief propagation (BP) (Sun et al, 2003; Felzenszwalb and Huttenlocher, 2006; Klaus et al, 2006) and dynamic programming (DP) (Ohta and Kanade, 1985; Gong and Yang, 2003; Torr and Criminisi, 2004), are often used to determine the local minimum of the energy function. Global methods render better quality of the estimated depth, but involve high computational complexity It makes them inapplicable for near or real-time applications.
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