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A Novel Approach for Computing Partial Similarity Between 3D Models

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Fuzzy Systems and Knowledge Discovery (FSKD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4223))

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Abstract

In this paper, we present our initial solution to partial similarity computation between arbitrary 3D polygon models. The task is considered as the estimation of similarity transformations between the query pattern and target object. Two steps accounting for the scaling and rotation/translation parts are carried out, facilitated by applying EMD (earth mover’s distance) to search the correspondence between focused point sets. In order to reduce the computation complexity involved in the second step, we use K-means algorithm to cluster the vertices of each model. We report our early experiments testing the efficiency of the proposed method on a small database as well as detailed discussions and the outline for the future work.

This paper is supported by National Science Fund Key Project of China under grant No. 60533050, National Science Fund Project of China under grant No. 60503056 and National Natural Science Funds of China for Innovative Research Groups under grant No.60021201.

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, W. (2006). A Novel Approach for Computing Partial Similarity Between 3D Models. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_51

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  • DOI: https://doi.org/10.1007/11881599_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45916-3

  • Online ISBN: 978-3-540-45917-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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