3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor (English)

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Abstract We present a novel 3D object retrieval method that relies upon a hybrid descriptor which is composed of 2D features based on depth buffers and 3D features based on spherical harmonics. To compensate for rotation, two alignment methods, namely CPCA and NPCA, are used while compactness is supported via scalar feature quantization to a set of values that is further compressed using Huffman coding. The superior performance of the proposed retrieval methodology is demonstrated through an extensive comparison against state-of-the-art methods on standard datasets.

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Ben-Chen, Mirela / Gotsman, Craig | 2008
3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor
Papadakis, Panagiotis / Pratikakis, Ioannis / Theoharis, Theoharis / Passalis, Georgios / Perantonis, Stavros | 2008
Isometry-invariant Matching of Point Set Surfaces
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Part Analogies in Sets of Objects
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Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval
Akgül, Ceyhun Burak / Sankur, Bülent / Yemez, Yücel / Schmitt, Francis | 2008
A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases
Lazaridis, Michalis / Daras, Petros | 2008
Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances
Berretti, Stefano / Bimbo, Alberto Del / Pala, Pietro / Mata, Francisco Josè Silva | 2008
A 3D Face Recognition Algorithm Using Histogram-based Features
Zhou, Xuebing / Seibert, Helmut / Busch, Christoph / Funk, Wolfgang | 2008
On-line and Open Platform for 3D Object Retrieval
Bonhomme, Benoit Le / Mustafa, B. / Celakovsky, Sasko / Preda, Marius / Preteux, Francoise / Davcev, D. | 2008