Abstract
A robust statistics approach to curvature estimation on scattered point cloud is presented. The basic idea of this method is fitting a surface to the local shape at a sample point in 3D and the curvatures are computed for this fitted surface. Within a Maximum Kernel Density Estimator framework, the best fitted surface for each point is obtained. Therefore the algorithm is robust with respect to noise and outliers. Experiments show that our method has achieved satisfactory results.
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