A global hypotheses verification method for 3d object recognition

Authors:
Aitor Aldoma;Federico Tombari;Luigi Di Stefano;Markus Vincze
Affiliations:
Vision4Robotics Group, ACIN, Vienna University of Technology, Austria;Computer Vision Lab., DEIS, University of Bologna, Italy;Computer Vision Lab., DEIS, University of Bologna, Italy;Vision4Robotics Group, ACIN, Vienna University of Technology, Austria
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Year:
2012

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Abstract

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. Our approach outperforms state-of-the-art on a challenging dataset including 35 household models obtained with the Kinect sensor, as well as on the standard 3D object recognition benchmark dataset.