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http://dx.doi.org/10.7848/ksgpc.2013.31.4.301

Multi-core-based Parallel Query of 3D Point Cloud Indexed in Octree  

Han, Soohee (Department of Geoinformatics Engineering)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.31, no.4, 2013 , pp. 301-310 More about this Journal
Abstract
The aim of the present study is to enhance query speed of large 3D point cloud indexed in octree by parallel query using multi-cores. Especially, it is focused on developing methods of accessing multiple leaf nodes in octree concurrently to query points residing within a radius from a given coordinates. To the end, two parallel query methods are suggested using different strategies to distribute query overheads to each core: one using automatic division of 'for routines' in codes controlled by OpenMP and the other considering spatial division. Approximately 18 million 3D points gathered by a terrestrial laser scanner are indexed in octree and tested in a system with a 8-core CPU to evaluate the performances of a non-parallel and the two parallel methods. In results, the performances of the two parallel methods exceeded non-parallel one by several times and the two parallel rivals showed competing aspects confronting various query radii. Parallel query is expected to be accelerated by anticipated improvements of distribution strategies of query overhead to each core.
Keywords
LiDAR; 3D Point Cloud; Query; Octree; Parallel Processing;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Chandler, D. (2010), Reduce False Sharing in .NET*, Intel Corporation, URL: http://software.intel.com/en-us/articles /reduce-false-sharing-in-net (last date accessed: 4 June 2013).
2 Cho, H., Cho, W., Park, J., and Song, N. (2008), 3D building modeling using aerial LiDAR data, Korean Journal of Remote Sensing, Vol. 24, pp. 141-152. (in Korean with English abstract)   과학기술학회마을   DOI
3 Marechal, L. (2009), Advances in octree-based allhexahedral mesh generation: handling sharp features, 18th International Meshing Roundtable, Salt Lake City, UT, USA, pp. 65-84.
4 Han, S., Lee, S., Kim, S. P., Kim, C., Heo, J., and Lee, H. (2011), A Comparison of 3D R-tree and octree to index large point clouds from a 3D terrestrial laser scanner, Korean Journal of Geomatics, Vol. 29, No. 1, pp. 531-537. (in Korean with English abstract)   과학기술학회마을   DOI   ScienceOn
5 Han, S., Kim, S., Jung, J. H., Kim, C., Yu, K., and Heo, J. (2012), Development of a hashing-based data structure for the fast retrieval of 3D terrestrial laser scanned data, Computers & Geosciences, Vol. 39, pp. 1-10.   DOI   ScienceOn
6 Han, S. (2013a). Design of Memory-Efficient Octree to Query Large 3D Point Cloud, Korean Journal of Geomatics, Vol. 31, No. 1, pp. 41-48. (in Korean with English abstract)   과학기술학회마을   DOI   ScienceOn
7 Han, S. (2013b). Enhancing Query Speed of 3D Points Structured by Octree Using Multi-threads, 2013 Spring Conference, Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Busan, Korea, pp. 381-382. (in Korean with English abstract)
8 OpenMP ARB (2013), The $OpenMP^{(R)}$ API specification for parallel programming, OpenMP Architecture Review Board, URL: http://openmp.org/wp/ (last date accessed: 21 May 2013).
9 PCL (2013), Point Cloud Library, Open Perception, URL: http://www.pointclouds.org/ (last date accessed: 21 August 2013).
10 Saxena, M., Finnigan, P. M., Graichen, C. M., Hathaway, A. F., and Parthasarathy, V. N. (1995), Octree-based automatic mesh generation for non-manifold domains, Engineering with Computers, Vol. 11, pp. 1-14.   DOI
11 Schnabel, R., Wahl, R., and Klein, R. (2007), Efficient RANSAC for point-cloud shape detection, Computer Graphics Forum, Vol. 26, pp. 214-226.   DOI   ScienceOn
12 Tian, T. and Shih, C-P (2012), Software Techniques for Shared-Cache Multi-Core Systems, Intel Corporation, URL: http://software.intel.com/en-us/articles/softwaretechniques- for-shared-cache-multi-core-systems (last date accessed: 4 June 2013)
13 Wang, M. and Tseng, Y.-H. (2004), Lidar data segmentation and classification based on octree structure, XXth ISPRS Congress, ISPRS, Istanbul, Turkey.
14 Wikipedia (2010), Schematic drawing of an octree, a data structure of computer science, Wikimedia Foundation, Inc., URL: http://en.wikipedia.org/wiki/Octree (last date accessed: 20 January 2013).
15 Wikipedia (2012), C dynamic memory allocation, Wikimedia Foundation, Inc., URL: http://en.wikipedia.org/wiki/C_ dynamic_memory_allocation (last date accessed: 20 January 2013).
16 Woo, H. ,Kang, E., Wang, S., and Lee, K. H. (2002), A new segmentation method for point cloud data. International Journal of Machine Tools and Manufacture, Vol. 42, pp. 167-178.   DOI   ScienceOn