1. Observer Relative Data Extraction Linas Bukauskas 3DVDM group Aalborg University, Denmark 2001

2. N/X VMMD Workshop 20012 Content Motivation Observer Relative Data Extraction –Visibility Range –Tree Structure –Visibility cases Experimental Results Related Work Conclusions Future Work

3. 2001N/X VMMD Workshop 20013 Motivation Unbounded Universe of objects –CAVE ® and Panorama creates fully immersed environment All objects are not visible at once –Catalog of stars 50GB (donor: Jim Gray @ Microsoft Research)

4. 2001N/X VMMD Workshop 20014 Motivation (cont`d) Visualization system can not handle all objects in the Universe –Rendering of the world is time consuming Observer is moving through the Universe –Arriving objects appear, leaving - disappear

5. 2001N/X VMMD Workshop 20015 Example of Moving Observer in 3D

6. 2001N/X VMMD Workshop 20016 Example of Moving Observer in 3D

7. 2001N/X VMMD Workshop 20017 ORDE Queries Objects that are visible Objects of specific visibility level Objects that will become (in) visible Objects that might be visible soon Objects that might be visible moving along the path

8. 2001N/X VMMD Workshop 20018 Distance Based Organization Create tree structure to access data Use distance based organization –Visibility Factor a parameter in a node The tree will order objects according the visibility factor –Second storage access structure B-Tree like structure

9. 2001N/X VMMD Workshop 20019 Distant Based Organization Visibility Factor Visible Objects

10. 2001N/X VMMD Workshop 200110 Distant Based Organization Fails Objects far away can be visible (if large) Near objects can be invisible (if small) 1 2

11. 2001N/X VMMD Workshop 200111 Observer Relative Data Extraction Requirements –Static Visibility Factor –Cluster/partition the space –Hierarchical structure –Second storage structure

12. 2001N/X VMMD Workshop 200112 Visibility Range (cont´d) Definition: Let O i be an object. The visibility range associated with the object, VR i (O i ) is: VR is a Minimal Bounding Square (MBS) Brightness and color can be incorporated

13. 2001N/X VMMD Workshop 200113 Visibility Range Overlapping object visibility ranges. MBS VR

14. 2001N/X VMMD Workshop 200114 The Tree Structure Hierarchical structure of MBRs and MBSs 1 2 3 4 7 6 5 1234567

15. 2001N/X VMMD Workshop 200115 The Tree Structure Querying: Overlaps 1 2 3 4 7 6 5 1234567

16. 2001N/X VMMD Workshop 200116 The Tree Structure (Cont´d) Two types of nodes: –MBRs internal –MBSs leaf nodes Pack more objects into leaf 1KB nodes 2D3D Internal256170 Leafnode341256

17. 2001N/X VMMD Workshop 200117 Three Cases of Queries Perfect –Visibility Ranges are as is Conservative –Visibility Ranges are enlarged Optimistic –Visibility Ranges are reduced

18. 2001N/X VMMD Workshop 200118 Perfect Case Point query –Observer position as input –Extracts only Visible Objects Window Query –Region of movement –Extracts now Visible Objects + Objects visible soon Scale factor: r = 1

19. 2001N/X VMMD Workshop 200119 Conservative Case Point query –Observer position as input –Surplus Visible Objects –does not extract exactly Visible Objects Window Query –More surplus Visible Objects Scale factor: r > 1

20. 2001N/X VMMD Workshop 200120 Optimistic Case Point query –Observer position as input –Very Visible Objects Window Query –Region as input –Ensure Visible Object extraction, surplus invisible. Scale factor: 0 < r < 1

21. 2001N/X VMMD Workshop 200121 Three Cases of Queries Perfect –Finds exactly visible objects for the observer Conservative –Finds visible objects with a buffer for the observer to move Optimistic –Optimistically extracts visible objects, with a surplus amount of invisible data.

22. 2001N/X VMMD Workshop 200122 Experiments R-Tree vs. VR-Tree –Universe 100x100 units –Varying size of data set 250.000 - 1 mio. –Largest VR span 1% and 10% of the Universe –Page size 1 KB –Implemented on GIST

23. 2001N/X VMMD Workshop 200123 R vs. VR –Tree 10 % of universe1% of Universe VR R

24. 2001N/X VMMD Workshop 200124 Supernovas Supernovas has impact in Optimistic case –Perfect & Conservative vs. Optimistic

25. 2001N/X VMMD Workshop 200125 Related Work R-Tree (A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching.1984) –X-Tree (S. Berchtold, D. A. Keim, and H.-P. Kriegel. The X-tree : An Index Structure for High-Dimensional data, 1996.) –SS-Tree (D. A. White and R. Jain. Similarity Indexing with the SS-tree. 1996) –SR-Tree (N. Katayama and S. Satoh. The SR-tree: An Index Structure for High- Dimensional Nearest Neighbor Queries.1997) –TPR-Tree (S. Saltenis, C. S. Jensen, S. T. Leutenegger, and M. A. Lopez. Indexing the Positions of Continuously Moving Objects, 2000)

26. 2001N/X VMMD Workshop 200126 Related Work (cont’d) Space partitioning –Kd-Tree, Quad/Oct-Trees –kdB-Tree (J. T. Robinson. The K-D-B-Tree: A Search Structure For Large Multidimensional Dynamic Indexes.1981) –LSD h Tree (A. Henrich. The LSD h -Tree: An Access Structure for Feature Vectors. 1998)

27. 2001N/X VMMD Workshop 200127 Conclusions Work in progress –Observer position dependant queries –Visibility Ranges –Three special cases of queries Perfect, Conservative, Optimistic –Empirical evaluation

28. 2001N/X VMMD Workshop 200128 Future Work Evaluate tree in a higher dimensions –Does it make sense in Virtual Reality setting? Incremental data extraction when moving –Incoming and leaving objects Retrieve data that will be visible along the path –Given a path points optimize data extraction Validate results with cases from the real life

29. 2001N/X VMMD Workshop 200129 Acknowledgment Michael Böhlen 3DVDM project members

30. 2001N/X VMMD Workshop 200130 Questions?

31. 2001N/X VMMD Workshop 200131 Experiments Numerical evaluation 250.00011.000 500.00022.000 1.000.00043.000 1.000.00055.000 1.000.0001110.000 1.000.00025100.000 # of objectsTime (s)visible