Abstract
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.
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Ramli, A., Ivrissimtzis, I. (2012). Bootstrap-Based Normal Reconstruction. In: Boissonnat, JD., et al. Curves and Surfaces. Curves and Surfaces 2010. Lecture Notes in Computer Science, vol 6920. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27413-8_38
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DOI: https://doi.org/10.1007/978-3-642-27413-8_38
Publisher Name: Springer, Berlin, Heidelberg
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