Shape based 3D model retrieval without query
Proceedings of the 6th ACM international conference on Image and video retrieval
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
An images-based 3d model retrieval approach
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Subspace methods for retrieval of general 3D models
Computer Vision and Image Understanding
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Similarity measuring is a key problem for 3D model retrieval. In this paper, we propose a novel shape descriptor "Thickness Histogram" (TH) by uniformly estimating thickness of a model using statistical methods. It is translation and rotation-invariant, discriminative to different shapes, and very efficient to compute with the Shape Distribution (SD) proposed by Osada etc. For high performance of the retrieval, we propose a robust method for translating the directional form of the statistical distribution to the harmonic representation. By summing up energies at different frequencies, a matrix shape signature is formed to provide an exhaustive characterization of 3D geometry. Experiments show that the performance of the statistical harmonic representation is among the top ones of existing shape descriptors.