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 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

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

Contributions Generate the representation from a noisy data set

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

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

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

Forward search and iterative refitting

Results Reconstruction of the fandisk model

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)

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