Abstract
Surface reconstruction from implicit data of sub-randomly distributed 3D points is the key work of extracting explicit information from LiDAR data. This paper proposes an approach of extended snake theory to surface reconstruction from LiDAR data. The proposed algorithm approximates a surface with connected planar patches. Growing from an initial seed point, a surface is reconstructed by attaching new adjacent planar patches based on the concept of minimizing the deformable energy. A least-squares solution is sought to keep a local balance of the internal and external forces, which are inertial forces maintaining the flatness of a surface and pulls of observed LiDAR points bending the growing surface toward observations. Experiments with some test data acquired with a ground-based LiDAR demonstrate the feasibility of the proposed algorithm. The effects of parameter settings on the delivered results are also investigated.
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References
Ackermann, F.: Airborne laser scanning—present status and future expectations. ISPRS Journal of Photogrammetry & Remote Sensing 54, 64–67 (1999)
Wehr, A., Lohr, U.: Airborne laser scanning—an introduction and overview. ISPRS Journal of Photogrammetry & Remote Sensing 54, 68–82 (1999)
Kass, M., Witkin, A., Terzopoupos, D.: Snakes: active contour models. International Journal of Computer Vision 1, 321–331 (1988)
Terzopoulous, G., Metaxas, D.: Dynamic 3D models with local and global deformations: deformable Superquadrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 703–714 (1991)
Filin, S., Pfeiper, N.: Segmentation of airborne laser scanning data using a slope adaptive neighborhood. ISPRS Journal of Photogrammetry and Remote Sensing 60, 71–80 (2006)
Hansen, W.V., Michaelsen, E., Thonnessen, U.: Cluster analysis and priority sorting in huge points clouds for building reconstruction. In: Proceedings of the 18th International Conference on Pattern Recognition, pp. 23–26 (2006)
Schuster, H.-F.: Segmentation of LiDAR data using tersor voting framework. In: International Archives of Photogrammetry and Remote Sensing, Istanbul (2004)
Wang, M., Tseng, Y.H.: LiDAR data segmentation and classification based on octree structure (2004)
Lucieer, A., Stein, A.: Texture-based landform segmentation of LiDAR imagery. International Journal of Applied Earth Observation and Geoinformation 6, 261–270 (2005)
Gruen, A., Wang, X.: CC-Modeler: a topology generator for 3-D city models. ISPRS Journal of Photogrammetry & Remote Sensing 53, 286–295 (1998)
Tognola, G., Parazzini, M., Ravazzani, P., Grandori, F., Svelto, C.: Adaptive algorithm for accurate reconstruction of solid surface from unorganized data. In: IEEE International Workshop on Imaging Systems and Techniques, pp. 44–47 (2004)
Filin, S.: Surface Clustering from Airborne Laser Scanning Data. International Archives of Photogrammetry and Remote Sensing, Part 3A/B 34, 119–124 (2002)
Bulpitt, A.J., Efford, N.D.: An efficient 3D deformable model with a self -optimising mesh. Image and Vision Computing 14, 573–580 (1996)
Montagnat, J., Delingette, H., Ayache, N.: A review of deformable surfaces: topology, geometry and deformation. Image and Vision Computing 19, 1023–1040 (2001)
Brovelli, M.A., Cannata, M.: Digital terrain model reconstruction in urban areas from airborne laser scanning data: the method and an example for Pavia (northern Italy). Computers & Geosciences 30, 325–331 (2004)
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Tseng, YH., Tang, KP., Chou, FC. (2007). Surface Reconstruction from LiDAR Data with Extended Snake Theory. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_37
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DOI: https://doi.org/10.1007/978-3-540-74198-5_37
Publisher Name: Springer, Berlin, Heidelberg
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