Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation

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 ...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Bogdan Matei, Ying Shan, Harpreet S. Sawhney, Yi Tan, Rakesh Kumar, Daniel F. Huber, Martial Hebert
• Keywords: Alignment-based Matching Techniques, PAMI 2006, Real 3D, Surface Descriptors