Browse > Article
http://dx.doi.org/10.7848/ksgpc.2013.31.6-2.611

Correction of Erroneous Model Key Points Extracted from Segmented Laser Scanner Data and Accuracy Evaluation  

Yoo, Eun Jin (Dept. of Geoinformation Engineering, Sejong University)
Park, So Young (Geo-spatial Information Team, National Disaster Management Institute)
Yom, Jae-Hong (Dept. of Geoinformation Engineering, Sejong University)
Lee, Dong-Cheon (Dept. of Geoinformation Engineering, Sejong University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.31, no.6_2, 2013 , pp. 611-623 More about this Journal
Abstract
Point cloud data (i.e., LiDAR; Light Detection and Ranging) collected by Airborne Laser Scanner (ALS) system is one of the major sources for surface reconstruction including DEM generation, topographic mapping and object modeling. Recently, demand and requirement of the accurate and realistic Digital Building Model (DBM) increase for geospatial platforms and spatial data infrastructure. The main issues in the object modeling such as building and city modeling are efficiency of the methodology and quality of the final products. Efficiency and quality are associated with automation and accuracy, respectively. However, these two factors are often opposite each other. This paper aims to introduce correction scheme of incorrectly determined Model Key Points (MKPs) regardless of the segmentation method. Planimetric and height locations of the MKPs were refined by surface patch fitting based on the Least-Squares Solution (LESS). The proposed methods were applied to the synthetic and real LiDAR data. Finally, the results were analyzed by comparing adjusted MKPs with the true building model data.
Keywords
LiDAR; Segmented surface patch; Model key feature; Surface fitting;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Park, S. (2012), Adaptive 3D Chain Code for Object Recognition and Modeling Using Airborne LiDAR Data, Master's thesis, Sejong University, Seoul, Korea, 133p. (in Korean with English abstract)
2 Park, S., Yoo, E., Lee, D.C., and Lee, Y. (2012), 3D shape descriptors for segmenting point cloud data, Journal of the Korean Society of Surveying, Photogrammetry and Cartography, Vol. 30, No. 6-2, pp. 643-651.   DOI   ScienceOn
3 Sampath, A. and Shan, J. (2008), Building roof segmentation and reconstruction from lidar point clouds using clustering techniques, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, Vol. XXXVII, Part B3a, pp. 279-284.
4 Schaffrin, B. and Snow, K. (2010), Total least-squares regularization of Tykhonov type and an ancient racetrack in Corinth, Linear Algebra and its Applications, Vol. 432, pp. 2061-2076.   DOI   ScienceOn
5 Schenk, T. (1999), Digital photogrammetry, Volume I: Background, Fundamentals, Automatic Orientation Procedures, TerraScience, Laurelville, Ohio, 428p.
6 Straub, C., Wang, Y., and Iercan, O. (2009), Airborne laser scanning: Methods for processing and automatic feature extraction for natural and artificial objects, In: Heritage, G. and Large A. (eds.), Laser Scanning for the Environmental Sciences, Wiley-Blackwell, Oxford, UK, pp. 115-132.
7 Szeliski, R. (2011), Computer Vision: Algorithms and Applications, Springer, London, 812p.
8 Ullman, S. (2000), High-Level Vision: Object Recognition and Visual Cognition, The MIT Press, Cambridge, MA, 412p.
9 Vosselman, G. and Dijkman, S. (2001), 3D building model reconstruction from point clouds and ground plans, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIV-3/W4, Annapolis, MD, pp. 37-43.
10 Vosselman, G. and Klein, R. (2010), Visualisation and structuring of point clouds, Vosselman, G. and Mass, H. In: Airborne and Terrestrial Laser Scanning, Whittles Publishing, Dunbeath, U.K., pp. 45-81.
11 Yoo, E., Yun, S., and Lee, D.C. (2012), Automatic 3D object digitizing and its accuracy using point cloud data, Journal of the Korean Society of Surveying, Photogrammetry and Cartography, Vol. 30, No. 1, pp. 1-10. (in Korean with English abstract)   과학기술학회마을   DOI   ScienceOn
12 Yoo, E., Lee, D.C., and Bae, T.S. (2011), 3D point cloud data modeling with total least-squares solution, Proceedings of 2011 KAGIS Spring Conference, 13-14 May, Busan, Korea, pp. 276-279. (in Korean)
13 Dongzhen, J., khoon, T., Zheng, Z., and Qi, Z. (2009), Indoor 3D modeling and visualization with a 3D terrestrial laser scanner, In: Lee, J. and Zlatanova, S. (eds.), 3D Geo- Information Sciences, Springer-Verlag, Berlin Heidelberg, pp. 247-255.
14 Kolbe, T. (2009), Representing and exchanging 3D city models with CityGML, In: Lee, J. and Zlatanova, S. (eds.), 3D Geo-Information Sciences, Springer-Verlag, Berlin Heidelberg, pp. 15-31.
15 Habib, A. (2013), Adaptive processing of LiDAR data for the extraction of planar and linear features, Invited Presentation at Sejong University, University of Seoul, and Yonsei University, Seoul, Korea.
16 Habib, A., Shin, S., Kim, C., and Al-Durgham, M. (2006), LIDAR-aided true orthophoto and DBM generation system, In: Abdul-Rahnam, A., Zlatanova, S., and Coors, V. (eds.), Innovations in 3D Geo Information Systems, Springer-Verlag, Berlin Heidelberg, pp. 47-65.
17 Kim, C., Zhai, R., Habib, A., Shin, S., Yoon, C., and Kim, K. (2009), Complex digital building model generation through the integration of photogrammetric data and LIDAR data, Proceedings of the ASPRS 2009 Annual Conference, 9-13 March, Baltimore, MD, unpaginated CD-ROM.
18 Lari, Z, Habib, A., and Kwak, E. (2011), An adaptive approach for segmentation of 3D laser point cloud, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-5/W12, ISPRS Workshop, 29-31 August 2011, Calgary, Canada, pp. 103-108.
19 Lee, J. and Lee, D.C. (2010), LiDAR Data segmentation using aerial Images for building modeling, Journal of the Korean Society of Surveying, Photogrammetry and Cartography, Vol. 28, No. 1, pp. 47-56.   과학기술학회마을
20 Li, J. and Guan H. (2011), 3D building reconstruction from airborne lidar point clouds fused with aerial imagery, In: Yang, X. (ed.), Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment, Wiley- Blackwell, West Sussex, U.K., pp. 75-91.
21 Avrahami, Y., Raizman, Y., and Doytsher, Y. (2008), A polygonal approach for automation in extraction of serial modular roofs, Photogrammetric Engineering & Remote Sensing, Vol. 74, No. 11, pp. 1365-1378.   DOI   ScienceOn
22 Lim, S. (2008), Automatic Building Extraction and 3D Modeling Using Airborne LiDAR Data, Master's thesis, Sejong University, Seoul, Korea, 102p. (in Korean with English abstract)
23 Meng, L., and Forberg, A. (2007), 3D building generalisation, In: Mackaness, W., Ruas, A., and Sarjakoski, L. (eds.), Generalisation of Geographic Information: Cartographic Modelling and Applications, Elsevier, Amsterdam, The Netherlands, pp. 211-231.
24 Mikolajczyk, K. and Schmid, C. (2004), Scale & affine invariant interest point detectors, International Journal of Computer Vision, Vol. 60, No. 1, pp. 63-86.   DOI
25 Boeters, R. (2013), Automatic Enhancement of CityGML LoD2 Models with Interiors and Its Usability for Net Internal Area Determination, Master's thesis, Delft University of Technology, Delft, Netherlands 119p.