Handle URI:
http://hdl.handle.net/10754/599408
Title:
Processing Terrain Point Cloud Data
Authors:
DeVore, Ronald; Petrova, Guergana; Hielsberg, Matthew; Owens, Luke; Clack, Billy; Sood, Alok
Abstract:
Terrain 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.
Citation:
DeVore 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.
Publisher:
Society for Industrial & Applied Mathematics (SIAM)
Journal:
SIAM Journal on Imaging Sciences
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
10-Jan-2013
DOI:
10.1137/110856009
Type:
Article
ISSN:
1936-4954
Sponsors:
This 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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorDeVore, Ronalden
dc.contributor.authorPetrova, Guerganaen
dc.contributor.authorHielsberg, Matthewen
dc.contributor.authorOwens, Lukeen
dc.contributor.authorClack, Billyen
dc.contributor.authorSood, Aloken
dc.date.accessioned2016-02-28T05:50:34Zen
dc.date.available2016-02-28T05:50:34Zen
dc.date.issued2013-01-10en
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.en
dc.identifier.issn1936-4954en
dc.identifier.doi10.1137/110856009en
dc.identifier.urihttp://hdl.handle.net/10754/599408en
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.en
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).en
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)en
dc.subjectAdaptive splinesen
dc.subjectCompressionen
dc.subjectHausdorff metricen
dc.subjectPoint cloudsen
dc.subjectSurface reconstructionen
dc.titleProcessing Terrain Point Cloud Dataen
dc.typeArticleen
dc.identifier.journalSIAM Journal on Imaging Sciencesen
dc.contributor.institutionTexas A and M, College Station, TX 77843, United Statesen
dc.contributor.institutionInstitute of Scientific Computation, College Station, United Statesen
dc.contributor.institutionAutomated Trading Desk, Mt Pleasant, United Statesen
kaust.grant.numberKUS-C1-016-04en
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