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    Processing Terrain Point Cloud Data

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    Type
    Article
    Authors
    DeVore, Ronald
    Petrova, Guergana
    Hielsberg, Matthew
    Owens, Luke
    Clack, Billy
    Sood, Alok
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2013-01-10
    Online Publication Date
    2013-01-10
    Print Publication Date
    2013-01
    Permanent link to this record
    http://hdl.handle.net/10754/599408
    
    Metadata
    Show full item record
    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.
    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).
    Publisher
    Society for Industrial & Applied Mathematics (SIAM)
    Journal
    SIAM Journal on Imaging Sciences
    DOI
    10.1137/110856009
    ae974a485f413a2113503eed53cd6c53
    10.1137/110856009
    Scopus Count
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