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.  A smart phone to see the interior of restaurants Applications  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 QtQt Q t+ 1 Q t+2 Q t+3 Q t+4 Q t+5 Q t+6 Speed QtQt Q t+ 1 Q t+2 Q t+3 Q t+4 Q t+5 Q t+6 A continuous query from a mobile client The details can be determined using the client’s motion

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

8. Example Wavelet Decomposition Base Mesh (M 0 ) Mesh (M 1 ) M0  M1M0  M1 v1v1 v2v2 v3v3 v1v1 v2v2 v3v3 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v4v4 v5v5 v6v6 Wavelet coefficient, d 4 = v 4 – (v 1 +v 2 )/2 = v 4 – v′ 4 v′ 4 v′ 5 v′6v′6

9. Example Wavelet Decomposition Mesh (M 1 ) v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 Mesh (M 2 ) v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v’ 12 v’ 14 v’ 15 v’ 10 v’ 13 v’ 11 v’ 8 v’ 7 v’ 9 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v 12 v 14 v 15 v 10 v 13 v 11 v8v8 v7v7 v9v9 M 1  M 2

10. 1 2 3 4 56 Incremental Retrieval (window) Q t-1 Q t AB C D A’B’ C’ D’ E F G

11. Data Retrieval in Multiple Resolutions O t  Q t  Q t-1 N t  Q t - Q t-1 r t  MapSpeedToResolution (s t ) If ( O t  ) then If (r t > r t-1 ) then R  Retrieve( {(O t, r t-1, r t ), (N t, 0, r t )} ) R  Retrieve( {(N t, 0, r t )} ) R  Retrieve( {(Q t, 0, r t )} ) else Algorithm: ContinuousRetrieval QtQt QtQt Q t-1 QtQt QtQt QtQt QtQt QtQt rtrt r t-1

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. Prediction QtQt Q t+1 0.5 0.2 0.3

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

15. p2p2 p3p3 p4p4 p1p1 p l = p 1 + p 2 p r = p 3 + p 4 a-1 nlnl nrnr n1n1 n2n2 p l = p 1 p r = p 2 Buffer Assignment: Generalized n3n3 n4n4

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 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6

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

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