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dc.contributor.authorDeVore, Ronald
dc.contributor.authorPetrova, Guergana
dc.contributor.authorHielsberg, Matthew
dc.contributor.authorOwens, Luke
dc.contributor.authorClack, Billy
dc.contributor.authorSood, Alok
dc.date.accessioned2016-02-28T05:50:34Z
dc.date.available2016-02-28T05:50:34Z
dc.date.issued2013-01-10
dc.identifier.citationDeVore R, Petrova G, Hielsberg M, Owens L, Clack B, et al. (2013) Processing Terrain Point Cloud Data. SIAM Journal on Imaging Sciences 6: 1–31. Available: http://dx.doi.org/10.1137/110856009.
dc.identifier.issn1936-4954
dc.identifier.doi10.1137/110856009
dc.identifier.urihttp://hdl.handle.net/10754/599408
dc.description.abstractTerrain point cloud data are typically acquired through some form of Light Detection And Ranging sensing. They form a rich resource that is important in a variety of applications including navigation, line of sight, and terrain visualization. Processing terrain data has not received the attention of other forms of surface reconstruction or of image processing. The goal of terrain data processing is to convert the point cloud into a succinct representation system that is amenable to the various application demands. The present paper presents a platform for terrain processing built on the following principles: (i) measuring distortion in the Hausdorff metric, which we argue is a good match for the application demands, (ii) a multiscale representation based on tree approximation using local polynomial fitting. The basic elements held in the nodes of the tree can be efficiently encoded, transmitted, visualized, and utilized for the various target applications. Several challenges emerge because of the variable resolution of the data, missing data, occlusions, and noise. Techniques for identifying and handling these challenges are developed. © 2013 Society for Industrial and Applied Mathematics.
dc.description.sponsorshipThis research was supported by the ARO/DoD contract W911NF-07-1-0185; the NSF grants DMS-0810869 and DMS-0900632; the Office of Naval Research contracts ONR-N00014-08-1-1113, ONR-N00014-09-1-0107, and ONR-N00014-11-1-0712; the AFOSR contract FA9550-09-1-0500; and the DARPA grant HR0011-08-1-0014. This publication is based in part on work supported by award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.subjectAdaptive splines
dc.subjectCompression
dc.subjectHausdorff metric
dc.subjectPoint clouds
dc.subjectSurface reconstruction
dc.titleProcessing Terrain Point Cloud Data
dc.typeArticle
dc.identifier.journalSIAM Journal on Imaging Sciences
dc.contributor.institutionTexas A and M, College Station, TX 77843, United States
dc.contributor.institutionInstitute of Scientific Computation, College Station, United States
dc.contributor.institutionAutomated Trading Desk, Mt Pleasant, United States
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2013-01-10
dc.date.published-print2013-01


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