Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data

  title={Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data},
  author={Chi-Keung Tang and G{\'e}rard G. Medioni},
  journal={IEEE Trans. Pattern Anal. Mach. Intell.},
ÐWe improve the basic tensor voting formalism to infer the sign and direction of principal curvatures at each input site from noisy 3D data. Unlike most previous approaches, no local surface fitting, partial derivative computation, nor oriented normal vector recovery is performed in our method. These approaches are known to be noise-sensitive since accurate partial derivative information is often required, which is usually unavailable from real data. Also, unlike approaches that detect signs of… CONTINUE READING
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