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Communication Dans Un Congrès Année : 2013

3D Shape Similarity Using Vectors of Locally Aggregated Tensors

Résumé

In this paper, we present an efficient 3D object retrieval method invariant to scale, orientation and pose. Our approach is based on the dense extraction of discriminative local descriptors extracted from 2D views. We aggregate the descriptors into a single vector signature using tensor products. The similarity between 3D models can then be efficiently computed with a simple dot product. Experiments on the SHREC12 commonly-used benchmark demonstrate that our approach obtains superior performance in searching for generic shapes.
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Dates et versions

hal-00832182 , version 1 (10-06-2013)

Identifiants

  • HAL Id : hal-00832182 , version 1

Citer

Hedi Tabia, David Picard, Hamid Laga, Philippe-Henri Gosselin. 3D Shape Similarity Using Vectors of Locally Aggregated Tensors. IEEE International Conference on Image Processing, Sep 2013, Melbourne, Australia. pp.2694-2698. ⟨hal-00832182⟩
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