Multiscale surface organization and description for free form object recognition

Abstract

We introduce an efficient, robust means to obtain reliable surface descriptions, suitable for free form object recognition, at multiple scales from range data. Mean and Gaussian curvatures are used to segment the surface into four saliency classes based on curvature consistency as evaluated in a robust multivoting scheme. Contiguous regions consistent in both mean and Gaussian curvature are identified as the most homogeneous segments, followed by those consistent in mean curvature but not Gaussian curvature, followed by those consistent in Gaussian curvature only. Segments at each level of the hierarchy are extracted in the order of size, large to small, such that the most salient features of the surface are recovered first. This has potential for efficient object recognition by stopping once a just sufficient description is extracted.