Refinement of disparity estimates through the fusion of monocularimage segmentations
McKeown, D.M., Jr.
Perlant, F.P.
Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA;
Abstract
The authors examine how estimates of three-dimensional scene
structure, as encoded in a scene disparity map, can be improved by the
analysis of the original monocular imagery. They describe the
utilization of surface illumination information provided by the
segmentation of the monocular image into fine surface patches of nearly
homogeneous intensity to remove mismatches generated during stereo
matching. These patches are used to guide a statistical analysis of the
disparity map based on the assumption that such patches correspond
closely with physical surfaces in the scene. Such a technique is quite
independent of whether the initial disparity map was generated by
automated area-based or feature-based stereo matching. Refinement
results on complex urban scenes containing various man-made and natural
features are presented, and the improvements due to monocular fusion
with a set of different region-based image segmentations are
demonstrated
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