Conference article

Using Curvilinear Grids to Redistribute Cluster Cells for Large Point Clouds

Daniel Schifner
Professur für Graphische Datenverarbeitung, Goethe Universität Frankfurt, Germany

Marcel Ritter
AirborneHydroMapping GmbH, Technikerstr 21a, Innsbruck/Universität Innsbruck, Technikerstr 13/25, Innsbruck, Austria

Dominik Steinhauser
AirborneHydroMapping GmbH, Technikerstr 21a, Innsbruck, Austria

Werner Benger
Universität Innsbruck, Technikerstr 13/25, Innsbruck, Austria

Download article

Published in: Proceedings of SIGRAD 2014, Visual Computing, June 12-13, 2014, Göteborg, Sweden

Linköping Electronic Conference Proceedings 106:2, p. 9-16

Show more +

Published: 2014-10-30

ISBN: 978-91-7519-212-3

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

Clustering data is a standard tool to reduce large data sets enabling real-time rendering. When applying a grid based clustering, one cell of a chosen grid becomes the representative for a cluster cell. Starting from a uniform grid in a projective coordinate system, we investigate a redistribution of points from and to neighboring cells. By utilizing this redistribution, the grid becomes implicitly curvilinear, adapting to the point cloud’s inhomogeneous geometry. Additionally to pure point locations, we enabled data fields to influence the clustering behaviour. The algorithm was implemented as a CPU and a GPU code. The GPU implementation uses GLSL compute shaders for fast evaluation and directly manipulates the data on the graphics hardware, which reduces memory transfers. Data sets stemming from engineering and astrophysical applications were used for benchmarking. Different parameters dependent on the geometric properties were investigated and performance was measured. The method turned out to reach interactivity for medium sized point clouds and still good performance for large point clouds. The grid based approach is fast, while being able to adapt to the point cloud geometry.

Keywords

No keywords available

References

[ABCO*01] ALEXA M., BEHR J., COHEN-OR D., FLEISHMAN S., LEVIN D., SILVA C. T.: Point Set Surfaces. In IEEE Visualization (2001), Ertl T., Joy K. I., Varshney A., (Eds.), IEEE Computer Society. [ahm] http://ahm.co.at.

[BH86] BARNES J., HUT P.: A hierarchical 0 (N log iV) forcecalculation algorithm. Nature (1986).

[CCSG12] CYRIL CRASSIN, SIMON GREEN: Octree-Based Sparse Voxelization Using the GPU Hardware Rasterizer. In OpenGL Insights, Cozzi P., Riccio C., (Eds.). CRC Press, July 2012, pp. 303–319. http://www.openglinsights.com/.


[DBS*13] DOBLER W., BARAN R., STEINBACHER F., RITTER M., NIEDERWIESER M., BENGER W., AUFLEGER M.: Die Zukunft der Gewässervermessung: Die Verknüpfung moderner und klassischer Ansätze: Airborne Hydromapping und Fächerecholotvermessung entlang der Rheins bei Rheinfelden. Wasser- Wirtschaft 9 (2013), 18–25.

[DT07] DECORO C., TATARCHUK N.: Real-time Mesh Simplification Using the GPU. In Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games (New York, NY, USA, 2007), I3D ’07, ACM, pp. 161–166.

[FAW10] FRAEDRICH R., AUER S., WESTERMANN R.: Efficient High-Quality Volume Rendering of SPH Data. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2010) 16, 6 (November-December 2010), to appear.

[GH98] GARLAND M., HECKBERT P. S.: Simplifying surfaces with color and texture using quadric error metrics. In IEEE Visualization (1998), pp. 263–269.

[Mon92] MONAGHAN J. J.: Smoothed particle hydrodynamics. Annual review of astronomy and astrophys. 30 (1992), 543–574.

[OGW*13] OTEPKA J., GHUFFAR S., WALDHAUSER C., HOCHREITER R., PFEIFER N.: Georeferenced Point Clouds: A Survey of Features and Point Cloud Management. ISPRS International Journal of Geo-Information 2, 4 (2013), 1038–1065.

[PC12] PENG C., CAO Y.: A GPU-based Approach for Massive Model Rendering with Frame-to-Frame Coherence. Comp. Graph. Forum 31, 2pt2 (May 2012), 393–402.

[PGK02] PAULY M., GROSS M., KOBBELT L. P.: Efficient Simplification of Point-sampled Surfaces. In Proceedings of the Conference on Visualization ’02 (Washington, DC, USA, 2002), VIS ’02, IEEE Computer Society, pp. 163–170.

[PMOK14] PFEIFER N., MANDLBURGER G., OTEPKA J., KAREL W.: OPALS - A framework for Airborne Laser Scanning data analysis. Computers, Environment and Urban Systems 45, 0 (2014), 125 – 136.

[RB12] RITTER M., BENGER W.: Reconstructing Power Cables From LIDAR Data Using Eigenvector Streamlines of the Point Distribution Tensor Field. Journal of WSCG 20, 3 (2012), 223–230.

[SHKS12] STEINHAUSER D., HAIDER M., KAPFERER W., SCHINDLER S.: Galaxies undergoing ram-pressure stripping: the influence of the bulge on morphology and star formation rate. Astronomy& Astrophysics 544 (July 2012), A54.

[SK12] SCHIFFNER D., KRÖMKER D.: Parallel treecutmanipulation for interactive level of detail selection. In 20th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (2012), vol. 20.

[Spr05] SPRINGEL V.: The cosmological simulation code gadget-2. Monthly Notices of the Royal Astronomical Society 364, 4 (Dec. 2005), 1105–1134.

[SWJ*05] SPRINGEL V., WHITE S. D. M., JENKINS A., FRENK C. S., YOSHIDA N., GAO L., NAVARRO J., THACKER R., CROTON D., HELLY J., PEACOCK J. A., COLE S., THOMAS P., COUCHMAN H., EVRARD A., COLBERG J., PEARCE F.: Simulating the Joint Evolution of Quasars, Galaxies and their Largescale Distribution. Nature (2005).

[Wil11] WILLMOTT A.: Rapid Simplification of Multi-Attribute Meshes. In High-Performance Graphics 2011 (August 2011).

Citations in Crossref