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| rdf:type
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| Description
| - 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). In spite of being always obtained in a certain order during the measurement, scanning data do not preserve this arrangement after their export, therefore, the procedure chosen for the searching of the neighbourhood of a point in a large point cloud (hundreds of thousands to millions of points) was the conversion of the problem of searching the neighbourhood in space (3D) into searching on a plane (2D); to this end, an algorithm was designed which is based on the application of coordinates recalculated into slope lengths, horizontal directions and zenith angles in the local coordinate system of the scanner. To make this procedure usable, untransformed data must be smoothed. The testing of the method usability was made as comparison of smoothed and reference objects.
- 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). In spite of being always obtained in a certain order during the measurement, scanning data do not preserve this arrangement after their export, therefore, the procedure chosen for the searching of the neighbourhood of a point in a large point cloud (hundreds of thousands to millions of points) was the conversion of the problem of searching the neighbourhood in space (3D) into searching on a plane (2D); to this end, an algorithm was designed which is based on the application of coordinates recalculated into slope lengths, horizontal directions and zenith angles in the local coordinate system of the scanner. To make this procedure usable, untransformed data must be smoothed. The testing of the method usability was made as comparison of smoothed and reference objects. (en)
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| Title
| - 3d Scanner Point Cloud Denoising By Near Points Surface Fitting
- 3d Scanner Point Cloud Denoising By Near Points Surface Fitting (en)
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| skos:prefLabel
| - 3d Scanner Point Cloud Denoising By Near Points Surface Fitting
- 3d Scanner Point Cloud Denoising By Near Points Surface Fitting (en)
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| skos:notation
| - RIV/68407700:21110/13:00205795!RIV14-MSM-21110___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21110/13:00205795
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - laser scanning; 3D scanning; denoising (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
| - Bellingham (stát Washington)
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| http://linked.open...i/riv/nazevZdroje
| - Videometrics, Range Imaging and Applications XII; and Automated Visual Inspection
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Smítka, Václav
- Štroner, Martin
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open...ain/vavai/riv/wos
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| http://linked.open.../riv/zahajeniAkce
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| issn
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| number of pages
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| http://bibframe.org/vocab/doi
| |
| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
| |
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