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Proceedings Paper

LIDAR data processing for scalable compression
Author(s): Ruben D. Nieves
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Paper Abstract

Compression of LIDAR point cloud offers many challenges to the signal processing community. Compression schemes must preserve both the numerical and geometrical aspects of the data, while dealing with the sparsely distributed threedimensional nature of it. Very few effective compression methods have been developed for this type of data, and only a handful of those methods offer the advantages of scalability. The focus of this research and development activity was to design and implement a series of preprocessing techniques that address the common obstacles found when pursuing scalable LIDAR point cloud compression. Three main areas being addressed are spatial scalability by means of effective indexing techniques; range reduction and redundancy exploitation; and resolution scalability by means of sub-band decomposition and sampling. These techniques will be combined with two different entropy encoding schemes –namely LZW and MQ encoding, yielding scalable 12:1 compression rates.

Paper Details

Date Published: 20 May 2013
PDF: 10 pages
Proc. SPIE 8731, Laser Radar Technology and Applications XVIII, 87310B (20 May 2013); doi: 10.1117/12.2029451
Show Author Affiliations
Ruben D. Nieves, Exelis Inc. (United States)


Published in SPIE Proceedings Vol. 8731:
Laser Radar Technology and Applications XVIII
Monte D. Turner; Gary W. Kamerman, Editor(s)

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