Model-Based Classification of Quadric Surfaces

Abstract

Model-based 3D object recognition systems using range imagery typically employ entirely data-driven procedures for segmentation and surface classification. However, some recognition environments may contain only objects whose surface types and parameters are known a priori and can therefore be exploited by the early-processing steps used in the recognition system. We propose a new suite of model-driven techniques for identification of quadric surfaces (cones, cylinders, and spheres) in segmented range imagery. The methods employ surface positions and surface normal estimates in combination with the known parameters of surfaces in a database of object models. Second-derivative quantities (i.e., surface curvatures) are not used. The free parameters of cylinders and spheres are accumulated using a Hough transform, and free parameters of cones are estimated using a regression procedure. Experiments are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, objects containing concave and convex surfaces, and noiseless and noisy synthetic range images of objects. Our experimental results show that the proposed surface classification methods can accurately recover surface parameters from both synthetic and real images, making them viable for environments with partial knowledge of surface type and parameters.