Object-space multiphase implicit functions

  • Authors:
  • Zhan Yuan;Yizhou Yu;Wenping Wang

  • Affiliations:
  • The University of Hong Kong;The University of Hong Kong, University of Illinois at Urbana-Champaign;The University of Hong Kong

  • Venue:
  • ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
  • Year:
  • 2012

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Abstract

Implicit functions have a wide range of applications in entertainment, engineering and medical imaging. A standard two-phase implicit function only represents the interior and exterior of a single object. To facilitate solid modeling of heterogeneous objects with multiple internal regions, object-space multiphase implicit functions are much desired. Multiphase implicit functions have much potential in modeling natural organisms, heterogeneous mechanical parts and anatomical atlases. In this paper, we introduce a novel class of object-space multiphase implicit functions that are capable of accurately and compactly representing objects with multiple internal regions. Our proposed multiphase implicit functions facilitate true object-space geometric modeling of heterogeneous objects with non-manifold features. We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. Comparisons demonstrate that our method achieves an error rate one order of magnitude smaller than alternative techniques.