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Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation
July 2006 (vol. 28 no. 7)
pp. 1111-1126
ASCII Text
x 
 
Bogdan Matei, Ying Shan, Harpreet S. Sawhney, Yi Tan, Rakesh Kumar, Daniel Huber, 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, pp. 1111-1126, July, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.148,
author = {Bogdan Matei and Ying Shan and Harpreet S. Sawhney and Yi Tan and Rakesh 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},
volume = {28},
number = {7},
issn = {0162-8828},
year = {2006},
pages = {1111-1126},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.148},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation
IS - 7
SN - 0162-8828
SP1111
EP1126
EPD - 1111-1126
A1 - Bogdan Matei,
A1 - Ying Shan,
A1 - Harpreet S. Sawhney,
A1 - Yi Tan,
A1 - Rakesh Kumar,
A1 - Daniel Huber,
A1 - Martial Hebert,
PY - 2006
KW - Three-dimensional object recognition
KW - hashing
KW - indexing
KW - pose estimation
KW - approximate nearest neighbor.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Three-dimensional object recognition, hashing, indexing, pose estimation, approximate nearest neighbor.
Citation:
Bogdan Matei, Ying Shan, Harpreet S. Sawhney, Yi Tan, Rakesh Kumar, Daniel Huber, 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, pp. 1111-1126, July 2006, doi:10.1109/TPAMI.2006.148
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