We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D scenes. Instead of verifying one hypothesis at a time, as done by most state-of-the-art 3D object recognition methods, we determine object and pose instances according to a global optimization stage based on a cost function which encompasses geometrical cues. Peculiar to our approach is the inherent ability to detect significantly occluded objects without increasing the amount of false positives, so that the operating point of the object recognition algorithm can nicely move toward a higher recall without sacrificing precision.
An implementation of the method is available in the Point Cloud Library, within the pcl_recognition module.
A. Aldoma, F. Tombari, L. Di Stefano, M. Vincze, “A Global Hypotheses Verification Method for 3D Object Recognition“, 12th European Conference on Computer Vision (ECCV), 2012 [PDF]