2012 | OriginalPaper | Buchkapitel
Bootstrap-Based Normal Reconstruction
verfasst von : Ahmad Ramli, Ioannis Ivrissimtzis
Erschienen in: Curves and Surfaces
Verlag: Springer Berlin Heidelberg
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We propose a bootstrap-based method for normal estimation on an unorganised point set. Experimental results show that the accuracy of the method is comparable with the accuracy of the widely used Principal Component Analysis. The main advantage of our approach is that the variance of the normals over the bootstrap samples can be used as a confidence value for the estimated normal. In a proposed application, we use the confidence values to construct a bilateral Gaussian filter for normal smoothing.