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

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

2001N/X VMMD Workshop 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 Microsoft Research)

2001N/X VMMD Workshop 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

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

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

2001N/X VMMD Workshop 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

2001N/X VMMD Workshop 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

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

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

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

2001N/X VMMD Workshop 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

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

2001N/X VMMD Workshop The Tree Structure Hierarchical structure of MBRs and MBSs

2001N/X VMMD Workshop The Tree Structure Querying: Overlaps

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

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

2001N/X VMMD Workshop 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

2001N/X VMMD Workshop 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

2001N/X VMMD Workshop 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

2001N/X VMMD Workshop 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.

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

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

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

2001N/X VMMD Workshop 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)

2001N/X VMMD Workshop 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)

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

2001N/X VMMD Workshop 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

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

2001N/X VMMD Workshop Questions?

2001N/X VMMD Workshop Experiments Numerical evaluation # of objectsTime (s)visible