Type
ArticleKAUST Department
Visual Computing Center (VCC)Date
2013-07Permanent link to this record
http://hdl.handle.net/10754/562832
Metadata
Show full item recordAbstract
This paper introduces a simple yet effective shape analysis mechanism for geometry processing. Unlike traditional shape analysis techniques which compute descriptors per surface point up to certain neighborhoods, we introduce a shape analysis framework in which the descriptors are based on pairs of surface points. Such a pairwise analysis approach leads to a new class of shape descriptors that are more global, discriminative, and can effectively capture the variations in the underlying geometry. Specifically, we introduce new shape descriptors based on the isocurves of harmonic functions whose global maximum and minimum occur at the point pair. We show that these shape descriptors can infer shape structures and consistently lead to simpler and more efficient algorithms than the state-of-the-art methods for three applications: intrinsic reflectional symmetry axis computation, matching shape extremities, and simultaneous surface segmentation and skeletonization. © 2012 IEEE.Citation
Youyi Zheng, Chiew-Lan Tai, Zhang, E., & Pengfei Xu. (2013). Pairwise Harmonics for Shape Analysis. IEEE Transactions on Visualization and Computer Graphics, 19(7), 1172–1184. doi:10.1109/tvcg.2012.309PubMed ID
23661011ae974a485f413a2113503eed53cd6c53
10.1109/TVCG.2012.309
Scopus Count
Related articles
- Object detection via structural feature selection and shape model.
- Authors: Zhang H, Bai X, Zhou J, Cheng J, Zhao H
- Issue date: 2013 Dec
- Generalized Local-to-Global Shape Feature Detection Based on Graph Wavelets.
- Authors: Li N, Wang S, Zhong M, Su Z, Qin H
- Issue date: 2016 Sep
- Rigid shape matching by segmentation averaging.
- Authors: Wang H, Oliensis J
- Issue date: 2010 Apr
- Intrinsic geometric scale space by shape diffusion.
- Authors: Zou G, Hua J, Lai Z, Gu X, Dong M
- Issue date: 2009 Nov-Dec
- Elastic model-based segmentation of 3-D neuroradiological data sets.
- Authors: Kelemen A, Székely G, Gerig G
- Issue date: 1999 Oct