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

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. Categories and Subject Descriptors (according to ACM CCS): I.5.4 [Pattern Recognition]: Computer Vision, H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval

• Type: Conference
• Year: 2008
• Where: 3DOR
• Authors: Panagiotis Papadakis, Ioannis Pratikakis, Theoharis Theoharis, Georgios Passalis, Stavros J. Perantonis
• Keywords: 3DOR 2008, Computer Graphics, Object Retrieval Method, Retrieval Methodology, Scalar Feature Quantization