Abstract
A fast and robust 3D retrieval method is proposed based on a novel weighted structural histogram representation. Our method has the following steps: 1) adaptively segment any 3D shape into a group of meaningful parts to generate local distribution matrixes, 2) integrate all the local distribution matrixes into a global distribution matrix, simultaneously considering their weight factors, and 3) retrieve 3D shapes by calculating the distance between their global distribution matrixes. Experimental results show that our method is effective and efficient for 3D shape retrieval and robust to translation, scaling or rotation transformations.
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Lu, T., Gao, R., Wang, T., Yang, Y. (2010). 3D Similarity Search Using a Weighted Structural Histogram Representation. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_32
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DOI: https://doi.org/10.1007/978-3-642-15702-8_32
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