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Simplification Architecture for Mobile VR Navigation Tradeoffs

This research explores the simplification of scene graphs for mobile VR, with a focus on navigation tradeoffs. It presents the problem definition, problem transformations, and experimental results.

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Simplification Architecture for Mobile VR Navigation Tradeoffs

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  1. A Simplification Architecture for Exploring Navigation Tradeoffs in Mobile VR Carlos D. Correa Ivan Marsic Rutgers University 2004

  2. Abstract • Application Scenarios • Context of this research • Scene graph Simplification • Problem definition • Problem transformations • Video • Stackable Solvers Architecture • Experimental Results Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  3. Application Scenarios Collaborative Editing Mobile Augmented Reality Charles Woodward, VTT Information Technology Wouter Pasman, Delft University of Technology Large Dataset Visualization in small devices Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  4. Interactive vs. Real-time Simplification Server Server Request Scene Scene New preferences Server Server Server Update Request Request Scene Delta scene Delta scene Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  5. Context Impostor Generation Progressive Meshes HLODs Quadric Error Metrics Image based impostors Impostor scheduling (combinatorial problem) Transmission/ Rendering 0.1, 1.0, 0.95, 0.4, … Benefit Heuristics Progressive transmission MPEG-4 NPR Remote rendering Funkhouser and Sequin, 1993 Maciel and Shirley, 1995 Mason and Blake, 2001 Erikson et al., 2001 Simpl. Error metrics User guided simpl. Regions of Interest TKP: Shaw and Cho, 1998 Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  6. Scene graph Simplification v1 v1 v1'v1'' v2 v3 v2' v2' v2'' v3' v3 v5 v4 v6 v7 v6' v7 v5' v6' v6'' v6' v6'' tire tire engine Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  7. Scene Completeness Scene Completeness No Scene Completeness Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  8. Speed-fidelity Tradeoff DP algorithm R = 1000 B = 15522 t = 3.43 ms Greedy algorithm R .= 1000 B = 9096 (58% of optimal fidelity) t = 0.31 ms Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  9. v1 v1'v1'' v3 v2 v3' v2'v2'' v5 v4 v6 v5' v6' Problem definition For each vertex vi define(bi, ri) and (bi', ri‘ ) Let: SOLVE: Max­­ {bixi + bi ' yi }, (1) Subject to rixi + ri'yiR(2) xi + yi 1 (3) xj + yi 1 if vi vj (4) xi xj + yj if vi vj (5) xi, yi = 0 or 1 (6) Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  10. u1 v1 v1' v1 v1'v1'' v1'' v2 v3 v3 u2 v3' v2'v2'' v3' v2' v2 v2'' Original scene graph Transformed to EMCTKP Problem transformations (EMCTKP) Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  11. (b1',r1') (b2'b3'b1'b1, r2'r3'r1'r1) (b1,r1) (b1',r1') (b2,r2) (b2',r2') (b3,r3) (b3',r3') (b3b3', r3r3') (b2b2', r2r2') EMCTKP + SC instance TKP instance Problem transformations (SC) Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  12. VIDEO Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  13. Proxy Server Scene Graph Interaction Simplified scene Mobile Client Benefit Metric Optimizer User preferences Resource Predictor S1 Simplification Algorithms and Transformations S2 Sn Stackable Solvers Architecture Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  14. Stackable Solvers Architecture (cont) Application addNode setSolution Optimizer addNode setSolution addNode Stackable Solver Transformer removeNode setSolution updateValue addNode setSolution Transformer setMaxResources addNode setSolution Algorithm Optimizer Optimizer Optimizer Optimizer EMCTKP Transformer EMCTKP Transformer Filtering Filtering EMCTKP Transformer Partial SC Transformer EMCTKP DP Algorithm SC Transformer TKP DP Algorithm EMCTKP Greedy Algorithm TKP DP Algorithm Suboptimal, No SC, Filtered elements Exact, Partial SC, Filtered elements Optimal, No SC Optimal, SC Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  15. Fidelity comparison Scene Completeness • Benefit ratio = benefitgreedy / benefitoptimal • Example: greedy algorithm is ~40% optimal for xcity44 with R=20000. • Same situation with NO SC: greedy algorithm finds optimal solution! • Greedy algorithms are more prone to fail (optimality below 50%) when: • Scene Completeness • Scene graph complexity No Scene Completeness Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  16. speed ratio 0.5 0.4 0.3 0.2 0.1 0 500 xcity44 1000 world xcity43 5000 xcity42 R 10000 xcity41 20000 Speed Comparison • Speed ratio = speedgreedy / speedoptimal • Example: greedy algorithm is ~2.8 times faster than optimal for xcity44 with R=1000, but 30 times faster for R=20000. • For small R, exact algorithm is comparable with greedy. • Exact algorithm computation time increases linearly with n and R. Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  17. Greedy heuristic + Optimal sub-solutions Greedy Exact Approximate fastest optimal NSC + essential impostors SC Partial SC NSC Most complete Most detailed Conclusions • Simplification as Impostor scheduling is a hard problem (NP-Complete) • A variation of TKP has been defined to represent the problem • Choice of algorithm result in speed-fidelity tradeoff • Preferences, e.g. scene completeness, also result in navigation tradeoff • Stackable Solvers Architecture provides a unified framework for exploring such tradeoffs and enabling mobile VR Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

  18. Thank You! More Info: http://www.caip.rutgers.edu/disciple http://www.caip.rutgers.edu/~cdcorrea/research Carlos D. Correa, Ivan Marsic. Rutgers University. 2004

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