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23 May 2013 3D scanner point cloud denoising by near points surface fitting
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The data obtained by 3D scanners with required higher accuracy and density contain disturbing noise, this noise makes the data processing, mainly by means of triangulated irregular networks using automated procedures, more complicated. The paper presents a new method of noise reduction based on natural redundancy of continuous objects and surfaces where, however, some deformation of the object shape occurs. The method involves a gradual choice of a selected number of the nearest points for each point of a scan, a selected surface is fitted with them and by the elongation or shortening of a beam with a given horizontal direction and the zenith angle onto the intersection with the surface a new (smoothed) position of the points is obtained. As the surface for fitting are used plane, polynomials of 2nd, 3rd and 4th degree. For the better calculation stability Chebyshev bivariant orthogonal polynomials are used. These surfaces are complemented by method using the mean. The solution of surface fitting may apply the least squares method with uniform weights or weights depending on distance, but also a robust method – the minimisation of the sum of absolute values of corrections (L1 norm).
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Václav Smítka and Martin Štroner "3D scanner point cloud denoising by near points surface fitting", Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87910C (23 May 2013);

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