Robust Moving Least-squares Fitting with Sharp Features Shachar Fleishman* Daniel Cohen-Or § Claudio T. Silva* * University of Utah § Tel-Aviv university
Surface reconstruction Noise Smooth surface Smooth sharp features Method for identifying and reconstructing sharp features
Point set surfaces (Levin ’98) Defines a smooth surface using a projection operator
Point set surfaces Defines a smooth surface using a projection operator Noisy point set The surface S is defined:
The MLS projection: overview Find a point q on the surfaces whose normal goes through the projected point x q is the projection of x
The MLS projection: overview Find a point q on the surfaces whose normal goes through the projected point x q is the projection of x Improve approximation order using polynomial fit
Sharp features Smoothed out Ambiguous
Sharp features Smoothed out Ambiguous – Classify
Projection near sharp feature
Classification Using outlier identification algorithm That fits a polynomial patch to a neighborhood
Classification Using outlier identification algorithm That fits a polynomial patch to a neighborhood
Statistics 101 Find the center of a set of points mean
Statistics 101 Find the center of a set of points Robustly using median mean median
Regression with backward search Loop – Fit a model – Remove point with maximal residual Until no more outliers
Regression with backward search Outliers can have a significant influence of the fitted model
Regression with forward search (Atkinson and Riani) Start with an initial good but crude surface – LMS (least median of squares) Incrementally improve the fit Monitor the search
Monitoring the forward search Residual plot
Monitoring the forward search Residual plot
Results Polynomial fit allows reconstruction of curved edges Input with missing data Reconstructed and corners Smooth MLS MLS w. edges
Results Noisy input Reconstructed input smooth sharp
Results Outliers are ignoredMisaligned regions are determined to be two regions Local decision may cause inconsistencies
Summary Classification of noisy point sets to smooth regions Application to PSS – Reconstruct surfaces with sharp features from noisy data – Improve the stability of the projection Local decisions may result different neighborhoods for adjacent points Can be applied to other surface reconstruction methods such as the MPU
Acknowledgements Department of Energy under the VIEWS program and the MICS office The National Science Foundation under grants CCF , EIA , and OISE A University of Utah Seed Grant The Israel Science Foundation (founded by the Israel Academy of Sciences and Humanities), and the Israeli Ministry of Science