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Recognizing Objects in Range Data Using Regional Point Descriptors
A. Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik
Proceedings of the European Conference on Computer Vision (ECCV), May, 2004.

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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.

Sponsor: DARPA E3D program
Grant ID: F33615-02-C-1265

Associated center: VASC
Associated lab/group: 3D Computer Vision Group
Associated project: Exploitation of 3-D Data

A. Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik, "Recognizing Objects in Range Data Using Regional Point Descriptors," Proceedings of the European Conference on Computer Vision (ECCV), May, 2004.

@inproceedings{Frome_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"
}