Content-based 3D model retrieval based on the spatial geometric descriptor

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Abstract

In this paper, we propose a novel shape descriptor for 3D objects, called spatial geometric descriptor (SGD), to represent the spatial geometric information of a 3D model by mapping its furthest distance, normal and area distribution onto spherical grids in a sequence of concentric shells. Then these spherical distribution functions are transformed to spherical harmonic coefficients which not only save the storage space but also provide multi-resolution shape description for any 3D model by adopting different dimensions for the coefficients. The feature vector extraction time can be reduced by adopting a single scan scheme on the mesh surface for a given 3D model. The retrieval performance is evaluated on the public Princeton Shape Benchmark (PSB) dataset and the experimental results show that our method not only outperforms Light Field Descriptor which is regarded as the best shape descriptor so far but also maintains an advantage of fast feature vector extraction procedure. © Springer-Verlag Berlin Heidelberg 2007.

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Wang, D., Zhang, J., Wong, H. S., & Li, Y. (2007). Content-based 3D model retrieval based on the spatial geometric descriptor. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4810 LNCS, pp. 685–694). Springer Verlag. https://doi.org/10.1007/978-3-540-77255-2_84

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