JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Improving Performance of File-referring Octree Based on Point Reallocation of Point Cloud File
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Improving Performance of File-referring Octree Based on Point Reallocation of Point Cloud File
Han, Soohee;
  PDF(new window)
 Abstract
Recently, the size of point cloud is increasing rapidly with the high advancement of 3D terrestrial laser scanners. The study aimed for improving a file-referring octree, introduced in the preceding study, which had been intended to generate an octree and to query points from a large point cloud, gathered by 3D terrestrial laser scanners. To the end, every leaf node of the octree was designed to store only one file-pointer of its first point. Also, the point cloud file was re-constructed to store points sequentially, which belongs to a same leaf node. An octree was generated from a point cloud, composed of about 300 million points, while time was measured during querying proximate points within a given distance with series of points. Consequently, the present method performed better than the preceding one from every aspect of generating, storing and restoring octree, so as querying points and memorizing usage. In fact, the query speed increased by 2 times, and the memory efficiency by 4 times. Therefore, this method has explicitly improved from the preceding one. It also can be concluded in that an octree can be generated, as points can be queried from a huge point cloud, of which larger than the main memory.
 Keywords
LiDAR;3D Point Cloud;Query;Octree;File-referring;
 Language
Korean
 Cited by
 References
1.
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)

2.
Maréchal, L. (2009), Advances in octree-based all-hexahedral mesh generation: handling sharp features, Proceedings of 18th International Meshing Roundtable, Salt Lake City, UT, USA, pp. 65-84.

3.
Han, S. (2013), Design of memory-efficient octree to query large 3D point cloud, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 31, No. 1, pp. 41-48. (in Korean with English abstract) crossref(new window)

4.
Han, S. (2014a), Implementation of file-referring octree for huge 3D point clouds, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 2, pp. 109-115. (in Korean with English abstract) crossref(new window)

5.
Han, S. (2014b), Enhancing query efficiency for huge 3D point clouds based on isometric spatial partitioning and independent octree generation, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 5, pp. 481-486. (in Korean with English abstract) crossref(new window)

6.
PCL (2015), Module octree, Point Cloud Library, http://docs.pointclouds.org/1.7.1/group__octree.html (last date accessed: 24 October 2015).

7.
RIEGL (2015a), RIEGL VZ-2000 Datasheet, RIEGL Laser Measurement Systems GmbH, http://www.riegl.com (last date accessed: 11 August 2015).

8.
RIEGL (2015b), RIEGL VUX-1 Series Infosheet, RIEGL Laser Measurement Systems GmbH, http://www.riegl.com (last date accessed: 11 August 2015).

9.
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. crossref(new window)

10.
Schnabel, R., Wahl, R., and Klein, R. (2007), Efficient RANSAC for point-cloud shape detection, Computer Graphics Forum, Vol. 26, pp. 214-226. crossref(new window)

11.
Wang, M. and Tseng, Y.-H. (2004), Lidar data segmentation and classification based on octree structure, Proceedings of XXth ISPRS Congress, ISPRS, Istanbul, Turkey.

12.
Wikipedia (2015), Brute-force search, Wikimedia Foundation, Inc., https://en.wikipedia.org/wiki/Brute-force_search (lastdate accessed: 11 August 2015).

13.
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. crossref(new window)