Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation

B. Matei, Ying Shan, H.S. Sawhney, Yi Tan, R. Kumar, Daniel Huber, and Martial Hebert
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 7, July, 2006, pp. 1111 - 1126.


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Abstract
We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the locality sensitive hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 16

Text Reference
B. Matei, Ying Shan, H.S. Sawhney, Yi Tan, R. Kumar, Daniel Huber, and Martial Hebert, "Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 7, July, 2006, pp. 1111 - 1126.

BibTeX Reference
@article{Huber_2006_5656,
   author = "B. Matei and Ying Shan and H.S. Sawhney and Yi Tan and R. Kumar and Daniel Huber and Martial Hebert",
   title = "Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation",
   journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
   pages = "1111 - 1126",
   month = "July",
   year = "2006",
   volume = "28",
   number = "7",
}