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A Motion-Aware Approach to Continuous Retrieval of 3D Objects (ICDE 2008)

A Motion-Aware Approach to Continuous Retrieval of 3D Objects (ICDE 2008). Mohammed Eunus Ali Rui Zhang Egemen Tanin Lars Kulik. Department of Computer Science and Software Engineering University of Melbourne, Australia. Outline. Applications and problem Our Motion-Aware approach

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A Motion-Aware Approach to Continuous Retrieval of 3D Objects (ICDE 2008)

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  1. A Motion-Aware Approach to Continuous Retrieval of 3D Objects(ICDE 2008) Mohammed Eunus Ali Rui Zhang Egemen Tanin Lars Kulik Department of Computer Science and Software Engineering University of Melbourne, Australia

  2. Outline • Applications and problem • Our Motion-Aware approach • Data representation and retrieval • Buffer • Index • Experiments • Conclusion

  3. Applications • A smart phone to see the interior of restaurants • Emerging more complex applications, e.g., tours using augmented reality • A rescue officer can see the structure of a building even if the building is on fire and filled with smoke

  4. Problem • Continuous retrieval of 3D objects in a window • Model: client-server • Bottle neck: bandwidth, especially when the view is moving fast

  5. Observation Qt+6 Qt+6 The details can be determined using the client’s motion A continuous query from a mobile client Qt+5 Qt+5 Qt+4 Qt+4 Qt+3 Qt+3 Speed Qt+2 Qt+2 Qt+1 Qt Qt+1 Qt Speed Speed

  6. Motion-Aware Approach • Motion-aware data retrieval (overall) • representing 3D objects in multiple resolutions (wavelets) • only retrieving necessary resolution (speed) • incremental retrieval (windows, resolutions) • Motion-aware buffer management (client) • prefetching • caching • Index for 3D objects in wavelets (server)

  7. Multi-resolution Representations Base mesh Progressively including details Figure: http://research.microsoft.com/~hoppe/

  8. Example Wavelet Decomposition v3 v3 v3 v6 v5 v6 v5 v′6 v′5 v′4 v2 v1 v2 v1 v2 v1 v4 v4 Mesh (M1) Base Mesh (M0) M0 M1 Wavelet coefficient, d4 = v4 – (v1+v2)/2 = v4 – v′4

  9. Example Wavelet Decomposition v3 v3 v3 v’12 v12 v11 v’11 v6 v5 v6 v5 v6 v5 v’10 v10 v’9 v9 v’8 v8 v’13 v13 v’15 v15 v2 v2 v1 v2 v1 v1 v’14 v’7 v14 v7 v4 v4 v4 Mesh (M1) M1 M2 Mesh (M2)

  10. Incremental Retrieval (window) F C’ D’ D C 3 G 6 5 Q t Q t-1 2 1 A’ E B’ 4 A B

  11. Algorithm: ContinuousRetrieval Ot Qt Qt-1 Nt Qt- Qt-1 rt MapSpeedToResolution (st) If ( Ot) then If (rt > rt-1) then R  Retrieve( {(Ot, rt-1, rt ), (Nt , 0, rt)} ) else R  Retrieve( {(Nt, 0, rt)} ) else R  Retrieve( {(Qt , 0, rt)} ) Data Retrieval in Multiple Resolutions rt Qt Qt-1 Qt Qt-1 Qt Qt-1 Qt rt-1 Qt Qt-1 Qt Qt-1 Qt-1 Qt

  12. Motion-Aware Buffer Management • We have a high-latency environment with decent computational capacity • Cache and pre-fetch objects that are very likely to be retrieved along the path of a client • Kalman-Filter is used in target tracking

  13. 0.3 Qt+1 0.5 0.2 Qt Prediction

  14. Buffer Assignment in One Dimension nopt a-1 1 Buffer: Given probabilities to move in one dimension to two directions Find: nopt Will maximize the Average Residency Time!

  15. p1 p4 p3 Buffer Assignment: Generalized a-1 pl =p1 + p2 pr =p3 + p4 nl nr pl =p1 pr =p2 p2 n1 n3 n2 n4

  16. Indexing 3D objects in Wavelets • A Naïve method: using a 4D R-tree • Position of the wavelet coefficient and • Magnitude of the coefficient • Each vertex needs a number of neighbor vertices too • Retrieval is a two step process: • Retrieve those coefficients that fall inside query window • Extend the query window to retrieve the neighbors • Our method: indexing the support region

  17. Support Regions of Wavelets v3 v6 v5 v2 v1 v4

  18. An Efficient Access Method w = 1.0 w Query : ( R, 1.0, 0.7 ) Query : ( R, 0.7, 0.0 ) x w = 0 y

  19. Experimental Setup • A city is augmented by complex 3D objects such as spheres, pyramids. • Three-dimensional objects are decomposed using wavelet-based techniques and stored in a server. • Clients make a tour in the city from a randomly selected source towards a destination: Using a Tram or on Foot

  20. Experiment Parameters • Data size: 20MB, 40MB, 60MB, 80MB • Query Frame: 5%, 15%, 15%, 20% in height and width • Wireless bandwidth 256Kbps and latency 200ms

  21. Continuous Retrieval Effect of speed on data retrieval

  22. Index Effect of speed

  23. Buffer Management (a) Cache hit rate (b) Data utilization Effect of buffer size

  24. Overall System Performance (a) Tram (b) Walk Query response time (Uniform)

  25. Overall System Performance (a) Tram (b) Walk Query response time (Zipf)

  26. Conclusions & Future Work • We proposed a motion-aware approach to continuous retrieval of 3D objects. Experiments shows that the motion aware techniques outperforms traditional ones and overall we achieve high improvement, especially when the view is moving fast. • Future work: • Server-side buffer management • Reflecting pathways in indices

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