A least square plane is fit through those n points and the value of this plane at the new position is the new pixel value. We cons In ordinary least squares, fit is defined as minimizing the squared vertical errors, that is finding the values of m and b that minimize the function F(m, b) = ∑(y i - mx i - b)². As in the simple regression case, this means finding the values of the b j coefficients for which the sum of the squares, expressed as follows, is minimum: where ŷ i is the y-value on the best fit line corresponding to x, …, x ik. Keywords: Data Approximation, Least Squares (LS), Weighted Least Squares (WLS), Moving Least Squares (MLS), Linear Sys However, reconstructing the PCL with respect to its native footprint has been a challenge. The algorithms are usually required in 3D applications where n = 3. Pcl least squares plane fitting April 15, 2016.
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