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Robust Moving Least-squares Fitting with Sharp Features

Robust Moving Least-squares Fitting with Sharp Features. Shachar Fleishman, Danieal Cohen-Or and Claudio Silva SIGGRAPH 2005. Difference . Levin’s MLS surface. Robust MLS . Currently Research Trend. Statistical method Using pattern recognition MPI

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Robust Moving Least-squares Fitting with Sharp Features

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  1. Robust Moving Least-squares Fitting with Sharp Features Shachar Fleishman, Danieal Cohen-Or and Claudio Silva SIGGRAPH 2005

  2. Difference Levin’s MLS surface Robust MLS

  3. Currently Research Trend • Statistical method • Using pattern recognition • MPI • Won-Ki Jeong, Ioannis Ivrissimtzis, Hans-Peter Seidel. “Neural Meshes: Statistical Learning based on Normals,” In Proc. Pacific Graphics, 2003. • H.Yamauchi, S.Lee, Y.Lee, Y.Ohtake, A.Belyaev, and H.-P.Seidel, Feature Sensitive Mesh Segmentation with Mean Shift, Shape Modeling International 2005

  4. Abstract • Robust moving least-squares technique for reconstructing a piecewise smooth surface from a noisy point cloud • Use robust statistics method • Forward-search paradigm • Define sharp features

  5. Contributions • Generate the representation from a noisy data set

  6. Background and related work • Surface reconstruction should be insensitive to noise • Generate a piecewise smooth surfaces which adequately represent the sharp features

  7. Surface Reconstruction • Pioneering work • Hoppe et al. [1994] • Create a piecewise smooth surface in a multi-phase process • Sharp features • Two polygons whit a crease angle that is higher than a threshold • Ohtake et al. [2003] • Surface representation • Defined by a blend of locally fitted implicit quadrics • Not sensitive to noise

  8. Robust statistics methods • Pauly et al. [2004] • Presented a method for measuring the uncertainty of a point set • Xie et al. [2004] • Extended the MPU technique to handle noisy datasets

  9. Forward search and iterative refitting

  10. Results • Reconstruction of the fandisk model

  11. Results • A reconstruction from a raw Deltasphere scan of a pipe (a) Input data (b) MLS (c) Robust MLS (d) Reconstructed surface (blue), input data (red)

  12. Results • Reconstruction of missing data (a) Input data; samples near the edge are missing (b) MLS (c) Robust MLS (d) Points that were projected to the edge are marked in yellow

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