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Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 1
Surface Classification Using Conformal Structures
Nice, France
October 13-October 16
ISBN: 0-7695-1950-4
Xianfeng Gu, University of Florida, Gainesville
Shing-Tung Yau, Harvard University
3D surface classification is a fundamental problem in computer vision and computational geometry. Surfaces can be classified by different transformation groups. Traditional classification methods mainly use topological transformation groups and Euclidean transformation groups. This paper introduces a novel method to classify surfaces by conformal transformation groups. Conformal equivalent class is refiner than topological equivalent class and coarser than isometric equivalent class, making it suitable for practical classification purposes. For general surfaces, the gradient fields of conformal maps form a vector space, which has a natural structure invariant under conformal transformations. We present an algorithm to compute this conformal structure, which can be represented as matrices, and use it to classify surfaces. The result is intrinsic to the geometry, invariant to triangulation and insensitive to resolution. To the best of our knowledge, this is the first paper to classify surfaces with arbitrary topologies by global conformal invariants. The method introduced here can also be used for surface matching problems.
Citation:
Xianfeng Gu, Shing-Tung Yau, "Surface Classification Using Conformal Structures," iccv, vol. 1, pp.701, Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 1, 2003
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