Robust Pointset Denoising of Piecewise-Smooth Surfaces through Line Processes

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dc.contributor.authorWei, Jiayien_US
dc.contributor.authorChen, Jiongen_US
dc.contributor.authorRohmer, Damienen_US
dc.contributor.authorMemari, Pooranen_US
dc.contributor.authorDesbrun, Mathieuen_US
dc.contributor.editorMyszkowski, Karolen_US
dc.contributor.editorNiessner, Matthiasen_US
dc.date.accessioned2023-05-03T06:10:03Z
dc.date.available2023-05-03T06:10:03Z
dc.date.issued2023
dc.description.abstractDenoising is a common, yet critical operation in geometry processing aiming at recovering high-fidelity models of piecewisesmooth objects from noise-corrupted pointsets. Despite a sizable literature on the topic, there is a dearth of approaches capable of processing very noisy and outlier-ridden input pointsets for which no normal estimates and no assumptions on the underlying geometric features or noise type are provided. In this paper, we propose a new robust-statistics approach to denoising pointsets based on line processes to offer robustness to noise and outliers while preserving sharp features possibly present in the data. While the use of robust statistics in denoising is hardly new, most approaches rely on prescribed filtering using data-independent blending expressions based on the spatial and normal closeness of samples. Instead, our approach deduces a geometric denoising strategy through robust and regularized tangent plane fitting of the initial pointset, obtained numerically via alternating minimizations for efficiency and reliability. Key to our variational approach is the use of line processes to identify inliers vs. outliers, as well as the presence of sharp features. We demonstrate that our method can denoise sampled piecewise-smooth surfaces for levels of noise and outliers at which previous works fall short.en_US
dc.description.number2
dc.description.sectionheadersReconstruction and Remeshing
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14752
dc.identifier.issn1467-8659
dc.identifier.pages175-189
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14752
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14752
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Point-based models
dc.subjectComputing methodologies
dc.subjectPoint
dc.subjectbased models
dc.titleRobust Pointset Denoising of Piecewise-Smooth Surfaces through Line Processesen_US
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