3D SHAPE SIMILARITY USING VECTORS OF LOCALLY AGGREGATED TENSORS

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 ap-proach obtains superior performance in searching for generic shapes. Index Terms — 3D Shape retrieval, Depth images, Bag of Fea-tures. 1