Recognizing Objects in Range Data Using Regional Point Descriptors

Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bulow, and Jitendra Malik
Proceedings of the European Conference on Computer Vision (ECCV), May, 2004.


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Abstract
Abstract. Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descrip- tors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

Keywords
3D, object recognition, spin image, shape context

Notes
Sponsor: DARPA E3D program
Grant ID: F33615-02-C-1265
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): 3D Computer Vision Group
Associated Project(s): Exploitation of 3-D Data

Text Reference
Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas Bulow, and Jitendra Malik, "Recognizing Objects in Range Data Using Regional Point Descriptors," Proceedings of the European Conference on Computer Vision (ECCV), May, 2004.

BibTeX Reference
@inproceedings{Huber_2004_4611,
   author = "Andrea Frome and Daniel Huber and Ravi Kolluri and Thomas Bulow and Jitendra Malik",
   title = "Recognizing Objects in Range Data Using Regional Point Descriptors",
   booktitle = "Proceedings of the European Conference on Computer Vision (ECCV)",
   month = "May",
   year = "2004",
}