Abstract
We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.
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Acknowledgements
We thank Dongming Yan for providing the code from Ref. [5] for comparison. The models in this paper were obtained from AIM@SHAPE and Princeton Segmentation Benchmark. The work was supported in part by a gift from Adobe System, Inc.
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Yixin Zhuang is an assistant researcher in the National Digital Switching System Engineering & Technological Research Center, China. He obtained his B.S. degree from Nanjing University of Aeronautics and Astronautics in 2008, and both M.S. and Ph.D. degrees from the National University of Defense Technology in 2011 and 2015, respectively. His research interests include computer graphics, and geometric modeling and processing.
Hang Dou studied computer science as an undergraduate in Zhejiang University, China, where he received his B.A. degree in 2010. He received his M.S. degree in computer science from the University of Iowa in 2013. He is currently a Ph.D. student in the Computer Science and Engineering Department in Washington University in St. Louis, USA. His primary research area is computer graphics, with particular interests in mesh processing, shape understanding, and fast rendering.
Nathan Carr is a principal scientist in Adobe Research leading a team of graphics researchers. He obtained his B. S. degree from the College of William & Mary, M.S. degree from Washington State University, and Ph.D. degree from the University of Illinois Urbana-Champaign. Since joining Adobe, he has produced numerous features for Adobe’s flagship products including Photoshop and Illustrator. The technologies span the domains of 3D photorealistic rendering, image processing, geometric modeling, and 3D printing. Nathan has authored dozens of academic papers and continues to guide research and development at Adobe.
Tao Ju is a professor in the Department of Computer Science and Engineering in Washington University in St. Louis, USA. He obtained his B.S. and B.A. degrees from Tsinghua University, China, in 2000, and his Ph.D. degree in computer science from Rice University in 2005. His research interests include computer graphics, geometry processing, and applications to biomedicine. He has received a number of grants from NSF and NIH, including an NSF CAREER Award. He has served as an associate editor for IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Computer-Aided Design, Graphical Models, and Computational Visual Media.
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Zhuang, Y., Dou, H., Carr, N. et al. Feature-aligned segmentation using correlation clustering. Comp. Visual Media 3, 147–160 (2017). https://doi.org/10.1007/s41095-016-0071-3
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DOI: https://doi.org/10.1007/s41095-016-0071-3