1. Adaptive Streaming and Rendering of Large Terrains using Strip Masks Joachim Pouderoux and Jean-Eudes Marvie IPARLA Project (LaBRI – INRIA Futurs) University of Bordeaux - France GRAPHITE’2005 talk by Tamy Boubekeur Joachim Pouderoux and Jean-Eudes Marvie IPARLA Project (LaBRI – INRIA Futurs) University of Bordeaux - France GRAPHITE’2005 talk by Tamy Boubekeur

2. Challenges  Growth of large database of accurate DEMs with high resolution textures acquired by satellites  Low memory devices  Slow CPU  No 3D accelerator

3. Previous Work In memory techniques  Irregular Meshes (e.g. [Hoppe 98]) Fewest polygons Fewest polygons Extremely CPU intensive Extremely CPU intensive  Bin-trees (e.g. [Lindstrom et al 96]) Simpler data structures / algorithms Simpler data structures / algorithms Still CPU intensive Still CPU intensive  Terrain Clipmap [Lossaso et al 2004] Store data in uniform 2D compressed grids Store data in uniform 2D compressed grids Mipmapping applied to terrains Mipmapping applied to terrains View dependent LOD using nesting of grids View dependent LOD using nesting of grids GPU optimized GPU optimized

4.  TerraVision II [Reddy et al 1997] Based on VRML inline and LOD nodes (induce data redundancy) Based on VRML inline and LOD nodes (induce data redundancy) View dependant only LOD View dependant only LOD Cracks appears on borders of the tiles Cracks appears on borders of the tiles  Extensions of bin-trees techniques eg.[Pajarola 1998] Dynamic scene management Dynamic scene management Still CPU intensive Still CPU intensive  BDAM [Cignoni et al 03] Precomputed regions  Decreased CPU cost Precomputed regions  Decreased CPU cost Temporal continuity difficult Temporal continuity difficult Previous Work Out-of-core techniques

5. Overview  Client / server approach The server mainly acts as a fileserver The server mainly acts as a fileserver  Adaptive terrain tiling The client manages a mosaic of small terrain patches The client manages a mosaic of small terrain patches  Adaptive terrain rendering Each tile is rendered according to an importance metric Each tile is rendered according to an importance metric

6. Terrain tiling Server side DEMs and textures are stored as a mosaic of compressed tiles

7. Adaptive Tiling  Belt of tiles are managed (loaded/cached or released) regarding to their distance to the camera and the current memory load.

8. Strip Masks  We pre-compute a set of n-1 triangle strips indexes (for n x n tiles) we call “strip masks”.  Each strip masks level as a known # of triangles  A shared data structure

9. LOD Metric  For each visible tile we compute a hint which represents the accuracy that should be used to render it. DistanceRoughnessMean (+)=

10. Budget Computation  From the analysis of the previous frames, we deduced a global budget of triangles allowed to render the scene at the target fps  Each tile t receives a budget of: triangles to make its rendering

11. Adaptive Rendering  For each tile, we choose the first strip mask which fits the triangle budget  The strip mask is used as the triangle strip index for the tile vertex buffer  To avoid to much popping, we go incrementally to the new tile mask

12. Inter-Level Transitions  Between two strip masks levels

13. Inter-Level Transitions ‘Fix’  We render a textured shadow plane under terrain ground Without shadow planes With shadow planes

14. Textures  Non-progressive textures (Bitmap formats) Direct download from the server Direct download from the server  Progressive textures (eg. PIT [Marvie03]) Allow texture streaming (real-time texture refinement) and a more accurate management Allow texture streaming (real-time texture refinement) and a more accurate management PIT: stores mipmap levels incrementally PIT: stores mipmap levels incrementally

15. Implementation  Magellan [Marvie04] A scene graph engine for adaptive and distributed rendering techniques A scene graph engine for adaptive and distributed rendering techniques  New VRML97 nodes Tiling: AutoGrid25D Tiling: AutoGrid25D Terrain: AutoElevationGrid (replace ElevationGrid) Terrain: AutoElevationGrid (replace ElevationGrid) #VRML V2.0 utf8 AutoGrid25D { baseName[ “Tiles\Terrain" ] baseExt[ "wrz" ] tileCountX7 tileCountZ7 sizeX2184.5 sizeZ2184.5 }

16. Puget Sound on PocketPC

17. Puget Sound on Desktop PC

18. Limitations  Poor visual continuity  No error analysis  Unnecessarily many triangles Assumes uniformly detailed terrain Assumes uniformly detailed terrain but, allows for optimal rendering throughput

19. Advantages  Simplicity  Real-time navigation in a terrain of unlimited size  Adaptive to memory and device capacity  Terrain walkthrough from handhelds to clusters

20. Future Work  Progressive terrain tiles  Use a more accurate terrain roughness evaluation  Strip masks templates which take continuity into account

21. Thank you!