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Simple primitive recognition via hierarchical face clustering

Abstract

We present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.

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Acknowledgements

This work was supported by the National Natural Science of Foundation for Outstanding Young Scholars (12022117), the National Natural Science Foundation of China (61872354), the Beijing Natural Science Foundation (Z190004), and the Intelligent Science and Technology Advanced subject project of University of Chinese Academy of Sciences (115200S001).

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Correspondence to Xiaohong Jia.

Additional information

Xiaolong Yang received his B.S. degree in information and computing science from Northwestern Polytechnical University in 2017 and is currently pursuing his M.S. and Ph.D. degrees at the Key Laboratory of Mathematics Mechanization, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences. His research interests are in computer graphics and computer vision.

Xiaohong Jia is an associate professor at the Key Laboratory of Mathematics Mechanization, Academy of Mathematics and Systems Science, Chinese Academy of Sciences. She received her Ph.D. and bachelor degrees from the University of Science and Technology of China in 2009 and 2004, respectively. Her research interests include computer graphics, computer aided geometric design, and computational algebraic geometry.

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Yang, X., Jia, X. Simple primitive recognition via hierarchical face clustering. Comp. Visual Media 6, 431–443 (2020). https://doi.org/10.1007/s41095-020-0192-6

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Keywords

  • quadric primitive extraction
  • mesh
  • hierarchical clustering