Approximate Conformal Parameterization of Point-Sampled Surfaces
نویسنده
چکیده
Drawing on recent machine learning work in dimensionality reduction, novel techniques for approximate conformal parameterization of point-set surfaces are introduced. An improved approximation of local tangent-spaces leads to a new method for computing Laplacian weights on point set neighbourhoods. These weights allow for linear minimization of the Dirichlet energy, and a robust one-parameter minimization of the Conformal energy. The latter technique repairs the well-known distortion of the “two-fixed-point” natural conformal parameterization [DMA02], enables free-boundary parameterization with uniform, authalic, and mean-value weights, and supports hybrid weight matrices to improve parameterization robustness.
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