Improving Completeness of Geometric Models from Terrestrial Laser Scanning Data

Clemens Nothegger


The application of terrestrial laser scanning for the documentation of cultural heritage assets is becoming increasingly common. While the point cloud by itself is sufficient for satisfying many documentation needs, it is often desirable to use this data for applications other than documentation. For these purposes a triangulated model is usually required. The generation of topologically correct triangulated models from terrestrial laser scans, however, still requires much interactive editing. This is especially true when reconstructing models from medium range panoramic scanners and many scan positions. Because of residual errors in the instrument calibration and the limited spatial resolution due to the laser footprint, the point clouds from different scan positions never match perfectly. Under these circumstances many of the software packages commonly used for generating triangulated models produce models which have topological errors such as surface intersecting triangles, holes or triangles which violate the manifold property. We present an algorithm which significantly reduces the number of topological errors in the models from such data. The algorithm is a modification of the Poisson surface reconstruction algorithm. Poisson surfaces are resilient to noise in the data and the algorithm always produces a closed manifold surface. Our modified algorithm partitions the data into tiles and can thus be easily parallelized. Furthermore, it avoids introducing topological errors in occluded areas, albeit at the cost of producing models which are no longer guaranteed to be closed. The algorithm is applied to scan data of sculptures of the UNESCO World Heritage Site Schönbrunn Palace and data of a petrified oyster reef in Stetten, Austria. The results of the method’s application are discussed and compared with those of alternative methods.


laser scanning, modeling, cultural heritage, automation, documentation, triangulation


P. Dorninger and C. Nothegger, “Automated Processing of Terrestrial Mid-Range Laser Scanner Data for Restoration Documentation at Millimeter Scale,” Proceedings of the 14th International Congress “Cultural Heritage and New Technologies,” Vienna, Austria: 2009.

H. Edelsbrunner and E.P. Mücke, “Three-dimensional Alpha Shapes,” 1994.

N. Amenta, S. Choi, and R.K. Kolluri, “The Power Crust,” Proceedings of the sixth ACM symposium on solid modeling and applications, Ann Arbor, Michigan, United States: ACM, 2001, pp. 249-266.

B. Curless and M. Levoy, “A Volumetric Method for Building Complex Models from Range Images,” Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, ACM, 1996, pp. 303-312.

H. Hoppe, “Surface Reconstruction from Unorganized Points,” PhD Thesis, University of Washington, 1994.

M. Bolitho, M.M. Kazhdan, R.C. Burns, and H. Hoppe, “Parallel Poisson Surface Reconstruction.,” ISVC (1), G. Bebis, R.D. Boyle, B. Parvin, D. Koracin, Y. Kuno, J. Wang, R. Pajarola, P. Lindstrom, A. Hinkenjann, M.L. Encarnação, C.T. Silva, and D.S. Coming, Eds., Springer, 2009, pp. 678-689.

M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” Proceedings of the fourth Eurographics symposium on Geometry processing, Eurographics Association Aire-la-Ville, Switzerland, Switzerland, 2006, pp. 6170.

C. Nothegger and P. Dorninger, “3D filtering of high-resolution terrestrial laser scanner point clouds for cultural heritage documentation,” Photogrammetrie, Fernerkundung, Geoinformation (PFG), vol. 1, 2009.

D.C. Hoaglin, F. Mosteller, and J.W. Tukey, Understanding Robust and Exploratory Data Analysis, WileyInterscience, 2000.

C. Nothegger and P. Dorninger, “Automated Modeling of Surface Detail from Point Clouds of Historical Objects,” 21st CIPA Symposium, Anticipating the Future of the Cultural Past, The Internationa l Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Athens: 2007, pp. 538 - 543.

P.J. Rousseeuw and K. van Driessen, “A Fast Algorithm for the Minimum Covariance Determinant Estimator,” Technometrics, vol. 41, 1999, pp. 212-223.

B. Li, R. Schnabel, R. Klein, Z. Cheng, G. Dang, and J. Shiyao, “Robust normal estimation for point clouds with sharp features,” Computers & Graphics, vol. 34, Apr. 2010, pp. 94-106.

B.J. Noye and R.J. Arnold, “Accurate finite difference approximations for the Neumann condition on a curved boundary,” Applied Mathematical Modelling, vol. 14, Jan. 1990, pp. 2-13.

A. Toselli and O. Widlund, Domain Decomposition Methods, Springer, 2004.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.