24 lines
1.4 KiB
C
24 lines
1.4 KiB
C
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#pragma once
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#include "lib_begin.h"
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namespace cgv {
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namespace math {
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//! Compute least squares normal from an array of 3D points.
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/*! Input is the number of points \c nr_points and a pointer \c _points to the point array. Points
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are assumed to be float tripples laying consecutively in the memory. Thus a vector \c P of type
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\c std::vector<cgv::math::fvec<float,3> > can be passed to this function according to
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estimate_normal_ls(P.size(), &P.front()[0], ...). The resulting 3D normal is written into the memory
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point to by \c _normal assuming space for 3 floats. If \c _evals is specified, also the eigenvalues from the
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fit are written to 3 floats pointed to by \c _evals. If \c _mean is specified, also the point mean through
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which the least squares plane goes is returned. If \c _evecs is specified, also the eigenvectors from the
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fit are written in 3 float trippels to memory pointed to by \c _evecs.*/
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extern CGV_API void estimate_normal_ls(unsigned nr_points, const float* _points, float* _normal, float* _evals = 0, float* _mean = 0, float* _evecs = 0);
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/// Weighted version of \c estimate_normal_ls with additional input \c _weights pointing to \c nr_points scalar weights.
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extern CGV_API void estimate_normal_wls(unsigned nr_points, const float* _points, const float* _weights, float* _normal, float* _evals = 0, float* _mean = 0, float* _evecs = 0);
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}
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}
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#include <cgv/config/lib_end.h>
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