On Gaussian filtering of scattered point clouds for surface metrology
On Gaussian filtering of scattered point clouds for surface metrology
The ISO standard Gaussian surface roughness filter cannot be directly applied to scattered point cloud surface measurement data, as it was originally developed for gridded areal surfaces. This study presents a Gaussian filtering approach that enables direct three-dimensional surface roughness characterisation from point cloud datasets, eliminating the need for conversion to a gridded format. The transmission characteristics of the proposed filter are analysed and shown to depend on both the amplitude and wavelength of the input signal. To mitigate this dependency, the (Formula presented) (Formula presented) parameter in the ISO standard Gaussian equation is adapted, allowing the proposed filter’s transmission behaviour to more closely match that of the ISO-standard Gaussian filter. The method’s performance is evaluated using both simulated point cloud data and measured surfaces produced by additive manufacturing, including a hemispherical geometry and an internal U-bend channel. The results demonstrate that the proposed approach enables direct 3D surface characterisation without data compression or simplification, effectively extending Gaussian filtering to the analysis of non-gridded measurement data.
Gaussian filtration, additive manufacturing, mesh, point cloud, surface roughness, x-ray computed tomography
Lifton, Joseph
9be501ec-2742-4ab6-8a5a-996c5b7c23ae
26 May 2026
Lifton, Joseph
9be501ec-2742-4ab6-8a5a-996c5b7c23ae
Lifton, Joseph
(2026)
On Gaussian filtering of scattered point clouds for surface metrology.
Surface Topography: Metrology and Properties, 14 (2), [025013].
(doi:10.1088/2051-672X/ae6ba0).
Abstract
The ISO standard Gaussian surface roughness filter cannot be directly applied to scattered point cloud surface measurement data, as it was originally developed for gridded areal surfaces. This study presents a Gaussian filtering approach that enables direct three-dimensional surface roughness characterisation from point cloud datasets, eliminating the need for conversion to a gridded format. The transmission characteristics of the proposed filter are analysed and shown to depend on both the amplitude and wavelength of the input signal. To mitigate this dependency, the (Formula presented) (Formula presented) parameter in the ISO standard Gaussian equation is adapted, allowing the proposed filter’s transmission behaviour to more closely match that of the ISO-standard Gaussian filter. The method’s performance is evaluated using both simulated point cloud data and measured surfaces produced by additive manufacturing, including a hemispherical geometry and an internal U-bend channel. The results demonstrate that the proposed approach enables direct 3D surface characterisation without data compression or simplification, effectively extending Gaussian filtering to the analysis of non-gridded measurement data.
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Published date: 26 May 2026
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© 2026 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence https://creativecommons.org/licenses/by/4.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Keywords:
Gaussian filtration, additive manufacturing, mesh, point cloud, surface roughness, x-ray computed tomography
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Local EPrints ID: 512756
URI: http://eprints.soton.ac.uk/id/eprint/512756
ISSN: 2051-672X
PURE UUID: 3585b5c8-d3b2-4ef1-b8f4-68827f1b3760
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Date deposited: 22 Jul 2026 17:06
Last modified: 07 Aug 2026 02:46
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Joseph Lifton
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