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dc.contributor.authorComino Trinidad, Marc
dc.contributor.authorAndújar Gran, Carlos Antonio
dc.contributor.authorChica Calaf, Antonio
dc.contributor.authorBrunet Crossa, Pere
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-01-22T12:24:54Z
dc.date.issued2018
dc.identifier.citationComino, M., Andujar, C., Chica, A., Brunet, P. Sensor-aware normal estimation for point clouds from 3D range scans. "Computer graphics forum", 2018, vol. 37, núm. 5, p. 233-243.
dc.identifier.issn0167-7055
dc.identifier.urihttp://hdl.handle.net/2117/127352
dc.description.abstractNormal vectors are essential for many point cloud operations, including segmentation, reconstruction and rendering. The robust estimation of normal vectors from 3D range scans is a challenging task due to undersampling and noise, specially when combining points sampled from multiple sensor locations. Our error model assumes a Gaussian distribution of the range error with spatially-varying variances that depend on sensor distance and reflected intensity, mimicking the features of Lidar equipment. In this paper we study the impact of measurement errors on the covariance matrices of point neighborhoods. We show that covariance matrices of the true surface points can be estimated from those of the acquired points plus sensor-dependent directional terms. We derive a lower bound on the neighbourhood size to guarantee that estimated matrix coefficients will be within a predefined error with a prescribed probability. This bound is key for achieving an optimal trade-off between smoothness and fine detail preservation. We also propose and compare different strategies for handling neighborhoods with samples coming from multiple materials and sensors. We show analytically that our method provides better normal estimates than competing approaches in noise conditions similar to those found in Lidar equipment.
dc.format.extent11 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Infografia
dc.subject.lcshThree-dimensional modeling
dc.titleSensor-aware normal estimation for point clouds from 3D range scans
dc.typeArticle
dc.subject.lemacInfografia tridimensional
dc.contributor.groupUniversitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica
dc.identifier.doi10.1111/cgf.13505
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13505
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23327208
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorComino, M., Andujar, C., Chica, A., Brunet, P.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameComputer graphics forum
upcommons.citation.volume37
upcommons.citation.number5
upcommons.citation.startingPage233
upcommons.citation.endingPage243


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