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Table of Contents
Local surface Normal Estimation
For some applications, not only is the local connectivity of a point region important, but so is the local surface normal for that particular neighborhood. Naturally, then, many point cloud libraries invole the calculation of the local normal vectors, either over the entire point set or for a subset of the point set. There are two related approaches for computing the normal vector.
PCA
The principal component analysis, or PCA, approach does not really perform principal component analysis, however it does share that same processing up to a certain point. PCA is effectively a method for finding a linear subspace that represents the data well. It involves eigenvalue/eigenvector analysis of the covariance matrix for a set of data.
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SVD
Noting the similarity of the PCA approach to the SVD calculations, one can immediately identify a similar procedure as the PCA one, but involving the SVD of a matrix.
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