Pre-processing procedures for raw point clouds from terrestrial laser scanners

Journal of Spatial Science

Volumn 52, Issue 2, 2007, Pages 65-74

  Bae, K H ^a,b   Belton, D ^a,b   Lichti, D D ^a,b  

^a CURTIN UNIVERSITY   (Australia)

^b Information ^*  (Australia)

Author keywords

Boundary; Edge detection; Point clouds; Pre processing; Terrestrial laser scanners

Indexed keywords

CLOUD; CLOUD CLASSIFICATION; DETECTION METHOD; LASER; METHODOLOGY; SCANNER; SEGMENTATION;

EID: 38549099135     PISSN: 14498596     EISSN: None     Source Type: Journal    
DOI: 10.1080/14498596.2007.9635123     Document Type: Article

References (17)

1. Bae, K.-H., 2006. “ Automated registration of unorganised point clouds from terrestrial laser scanners ”. 161Perth, Australia:Department of Spatial Sciences, Curtin University of Technology. Ph.D. dissertation
2. Belton, D., and Lichti, D. D., Classification and segmentation of terrestrial laser scanner point clouds using local variance information. Proceedings of ISPRS Commission V Symposium on Image Engineering and Vision Metrology. pp. 44–49.
3. Chikatsu, H., and Yokoyama, H., Robust filtering for topographic surveying by terrestrial laser scanner. Proceedings of optical 3-d measurement techniques. pp. 338–345.
4. Dold, C., and Brenner, C., Registration of terrestrial laser scanning data using planar patches and image data. Proceedings of ISPRS Commission V Symposium on Image Engineering and Vision Metrology. pp. 78–83.
5. Dyn, N., Hormann, K., Kim, S.-J., and Levin, D., Optimizing 3D triangulations using discrete curvature analysis. Proceedings of Mathematical methods for curves and surfaces. 2000, Oslo. pp. 135–146.
6. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., and Stuetzle, W., 1992. Surface reconstruction from unorganized points. Computer Graphics, 26:71–78.
7. Johnson, R. A., and Wichern, D. W., 2002. Applied Multivariate Statistical Analysis, New Jersey, USA:Prentice Hall.
8. Wang, M., and Tseng, Y.-H., LIDAR data segmentation and classification based on octree structure. Proceedings of the ISPRS congress. pp. 286–291.
9. Pauly, M., Mitra, N. J., and Guibas, L. J., Uncertainty and variability in point cloud surface data. Proceedings of Eurographics Symposium on Point-based Graphics. http://citeseer.ist.psu.edu/pauly04uncertainty.html, accessed on 16th April 2006
10. Pu, S., and Vosselman, G., Automatic extraction of building features from terrestrial laser scanning. Proceedings of ISPRS Commission V Symposium on Image Engineering and Vision Metrology. pp. 5–9.
11. Rabbani, T., Dijkman, S., van den Heuvel, F., and Vosselman, G., 2007. An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 61:355–370.
12. Schuster, H. F., Segmentation of lidar data using the tensor voting framework. Proceedings of the 20th ISPRS Congress. Istanbul, Turkey. pp. 1079–1084.
13. Sharkarji, C. M., 1998. Least-squares fitting algorithms of the NIST algorithm testing system. Journal of research of the National Institute of Standard and Technology, 103 (6):633–641.
14. Sun, Y, Page, D. L., Paik, J. K., Koschan, A., and Abidi, M. A., Triangle meshes-based edge detection and its application to surface segmen-tation and adaptive surface smoothing. Proceedings on IEEE International Conference on Image Processing III. pp. 825–828.
15. Tang, C. K., Medioni, P., Mordohai, P., and Tong, W S., 2004. First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (5):594–611.
16. Von Hansen, W, Michaelsen, E., and Thoennessen, U., Cluster analysis and priority sorting in huge point clouds for building reconstruction. Proceedings of International Conference on Pattern Recognition (ICPR 2006. pp. 23–26.
17. Vosselman, G., 2000. Slope based filtering of laser altimetry data. International Archives of the Photogrammetry and Remote Sensing (IAPRS), XXXIII:935–942.