Transforming Geodata for Immersive Visualisation

Transforming Geodata for Immersive Visualisation

Transforming Geodata for Immersive Visualisation M. Wiedemann 1 C. Anthes 1 H.-P. Bunge 2 B.S.A. Schuberth 2 D. Kranzlmüller 1 1 Centre for Virtual Reality and Visualisation Leibniz Supercomputing Centre 2 Department of Earth and Environmental Sciences Ludwig Maximilians Universität escience, 2015

What is Immersive Visualisation? What does it mean? Using Virtual Reality (VR) technology for scientific or information visualisation VR characteristics Real-time interactive User centered Typically stereoscopic Head tracking leads to intuitive and playful analysis Combination of stereoscopy and head motion fosters construction of mental spatial image

Hardware Requirements Potential displays Head-Mounted-Displays (HMD): Oculus Rift, HTC Vive,... Projection displays: Tracked Powerwalls, Curved Screens,... Spatially immersive displays: CAVE Environments Potential input devices Tracking system: Optical, inertial sensors Wand: Controller, Joypad Mobile devices: Tablet, Smartphone Source: https: //www1.oculus.com/order/ Source: http://www. ar-tracking.com/products/ interaction/flystick3/ Source: http://cdn.phys.org/newman/ gfx/news/2012/nexus10.jpg

Spatially Immersive Displays I Based on concepts by Carolina Cruz-Neira I 3 walls, floor and ceiling back-projected I Optical position tracking Source: http://crvm.ism.univ-amu.fr/images/ Schema_CAVE_en.png Immersive Visualisation Visualisation Application Present Visualisation Summary

Using CAVE-like Environments Why CAVE-like environments? Close to full field of view Walk within the data (locomotion) Multi-user Self perception Source: https://blog.leapmotion.com/ wp-content/uploads/2014/08/mount-fov.png

General Workflow Abstract Overview Interaction Input Input Input Display I/O Optimise I/O Display Display.. Map multiple inputs to multiple outputs

Input Simulation Data Mantle circulation model for simulation of Earth s mantle (calculated on SuperMUC) Simulation data including large-scale thermal structure 90 million points ETOPO1 topography dataset Additional 233 million points Visualisation of thermal data

Necessity for Optimisation Challenges Real-time requirement (120 frames per second: 60 per eye) Image synchronisation Current setup allows rendering of 1 million triangles Visual clutter Data reduction needed

Implementation 3ds Max ETOPO1 Temperature Distribution I/O Optimisation I/O OpenSG Application Interaction

Optimisation Iso surfacing Decimation Clustering

Rendered Images

Cave Images

Additional features Visual Helpers I Spherical grid at 410km and 660km below surface I Visualisation of Earth s core I Projection of surface texture on core I Phong shader for emphasis of structure Immersive Visualisation Visualisation Application Present Visualisation Summary

Video Source: Bayerischer Rundfunk - Rundschau 29.06.2015

Summary Data optimisation enabling immersive visualisation Intuitive insight for scientists Better understanding of simulation data Outlook Time dependent data visualisation Beachball representation for seismic events Visualise lithosphere Source: http://www.bssaonline.org/ content/92/1/376/f8.large.jpg

For Further Reading B. S. A. Schuberth, H.-P. Bunge, G. Steinle-Neumann, C. Moder, and J. Oeser, Thermal versus elastic heterogeneity in high-resolution mantle circulation models with pyrolite composition: High plume excess temperatures in the lowermost mantle, Geochemistry, Geophysics, Geosystems, vol. 10, no. 1, p. Q01W01, Jan. 2009. C. Cruz-Neira, D. J. Sandin, T. A. Defanti, R. V. Kenyon, and J. C. Hart, The cave: Audio visual experience automatic virtual environment, Communications of the ACM, vol. 35, no. 6, pp. 64 72, June 1992. D. Reiners, Opensg: A scene graph system for flexible and efficient realtime rendering for virtual and augmented reality applications, Ph.D. dissertation, Technische Universität Darmstadt, Mai 2002. Appendix

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