Transforming Geodata for Immersive Visualisation

Transcription

1 Transforming Geodata for Immersive Visualisation

2 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

3 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

4 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: ar-tracking.com/products/ interaction/flystick3/ Source: gfx/news/2012/nexus10.jpg

5 Spatially Immersive Displays I Based on concepts by Carolina Cruz-Neira I 3 walls, floor and ceiling back-projected I Optical position tracking Source: Schema_CAVE_en.png Immersive Visualisation Visualisation Application Present Visualisation Summary

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

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

8 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

9 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

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

11 Optimisation Iso surfacing Decimation Clustering

12 Rendered Images

13 Cave Images

14 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

15 Video Source: Bayerischer Rundfunk - Rundschau

16 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: content/92/1/376/f8.large.jpg

17 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 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 , June 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 Appendix

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