Object recognition and pose estimation from 3D-geometric relations

We present a method for object recognition and pose estimation from noisy range data as provided by stereo processing. From the range dates, points of high surface curvature are estimated. By comparing three-point geometric relations, hypothetical correspondences are established between data and model points of high curvature. The hypothetical correspondences give rise to pose hypotheses which are evaluated with respect to the raw range data using a crude surface model. We show examples that demonstrate the method's tolerance to noise and occlusion.