From point cloud to surface: modeling structures in laser scanner point clouds
- Pablo Rodríguez Gonzálvez 1
- Diego González Aguilera 1
- Javier Gómez Lahoz 1
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1
Universidad de Salamanca
info
ISSN: 1682-1777
Year of publication: 2007
Volume: 36
Part: 3
Pages: 338-343
Congress: ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007
Type: Conference paper
Abstract
The automatic modeling of precise structures from randomly distributed laser points derived from laser scanner data is a very hard problem, not completely solved and problematic in case of incomplete, noisy and sparse data. The generation of polygonal models that can satisfy high modeling and visualization demands is required in different applications, like architecture, archaeology, city planning, virtual reality applications and other graphics applications. The goal is always to find a way to create a computer model of an object which best fits the reality. Polygons are usually the ideal way to accurately represent the results of measurements, providing an optimal surface description. While the generation of digital terrain models has a long tradition and has found efficient solutions, the correct 3D modeling of closed surfaces or free-form objects is of recent nature, a not completely solved problem and still an important issue investigated in many research activities. In this paper we develop an approach for converting a laser scanner point cloud into a realistic 3D polygonal model that can satisfy high modeling and visualization demands. Close range photogrammetry deals since many years with manual or automatic image measurements. Now laser scanners are also becoming a standard source for input data in many application areas, providing millions of points. As a consequence, the problem of generating high quality polygonal models of objects from randomly distributed laser points is getting more and more attention. After reviewing some results in this context, we will describe a full approach for turning a usual unstructured point cloud into a consistent polygonal model. Finally, the polygonal model is turned into a hierarchical nodes network similar to VRML. A novel laserscanning processing tool, LSM3D (Laser Scanner Modeling 3D), has been developed and tested over different examples related with architectonic buildings.
Bibliographic References
- Ballard D. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122, 1981.
- Barnard S., 1983. Interpreting perspective images. Artificial Intelligence, vol. 21.
- Douglas D. & Peucker T. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10(2), 112-122 (1973)
- Jiang, X., Bunke, H. and Meier, U., 2000. High-level feature based range image segmentation. Image and Vision Computing, 18(10), pp. 811/822.
- Min J., Powell M. and Bowyer K. Automated performance evaluation of range image segmentation algorithms. Systems, Man and Cybernetics, Part B, IEEE Transactions on, vol. 34, nº 1, 2004.
- Sithole, G., Vosselman, G., 2004. Experimental Comparison of Filter Algorithms for Bare Earth Extraction from Airborne Laser Scanning Point Clouds. ISPRS Journal of Photogrammetry and Remote Sensing 59 (1-2): 85-101.
- Watson D. Computing the n-dimensional Delaunay Tessellation with Application to Voronoi Polytopes. Computer Journal 24(2):167–172, 1981.
- Bellon O. R. and Silva L. New improvements to range image segmentation by edge detection. Signal Processing Letters, IEEE, vol.9, nº 2, 2002.
- Chew L. Guaranteed-Quality Triangular Meshes. Technical Report TR-89-983, Department of Computer Science, Cornell University, 1989.
- Curless, B. Levoy, M. 1996: A volumetric method for building complex models from range images. ACM Proc. Of Siggraph, pp. 303-312.
- Fischler, M. A., and R. C. Bolles, 1981. Random sample consensus. CM Communications.
- Han S. and Medioni G.: Triangular NURBS surface modeling of scattered data. Proceedings of the 7th conference on Visualization '96 table of contents. San Francisco, California, United States, 1996.
- Krishnamurthy V. and Levoy M. Fitting smooth surfaces to dense polygon meshes. Proceedings of SIGGRAPH 96, pages 313–324, August 1996. Held in New Orleans, Louisiana.
- Oda K., Wei Lu Osamu Uchida Takeshi Doihara: Triangle- based visibility analysis and true orthoimage generation. International Congress of ISPRS. Istambul 2004.
- Palagyi K. and A. Kuba, A Parallel 3D 12-Subiteration Thinning Algorithm, pp. 199-221.
- Polis M. and D. McKeown: Iterative TIN Generation from Digital Elevation Models, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Illinois, 1992.
- Pottmann H., Stefan Leopoldseder, Johannes Wallner and Martin Peternell: Recognition and reconstruction of special surfaces from point clouds. ISPRS archives 2002.
- Shewchuk J.: Delaunay Refinement Algorithms for Triangular Mesh Generation. Department of Electrical Engineering and Computer Science. University of California at Berkeley, 2001.
- Vosselman G., B.G.H Gorte, G. Sithole, T. Rabbani : Recognising structures in laser scanner point clouds. International ISPRS Congress. Istambul (Turkey) 2004