Recognizing Objects in Range Data Using Regional Point Descriptors
Conference Paper, Proceedings of (ECCV) European Conference on Computer Vision, pp. 224 - 237, May, 2004
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.
BibTeX
@conference{Frome-2004-8914,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 (ECCV) European Conference on Computer Vision},
year = {2004},
month = {May},
pages = {224 - 237},
keywords = {3D, object recognition, spin image, shape context},
}
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