Controlling Memory Consumption of Hierarchical Radiosity with Clustering

1. Controlling Memory Consumption ofHierarchical Radiosity with Clustering Xavier Granier George Drettakis iMAGIS -GRAVIR/IMAG-INRIA iMAGIS is a joint project of CNRS/INRIA/UJF/INPG Graphics Interface 99

2. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

3. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

4. Motivation • Global illumination of large scenes • Hierarchical Radiosity with Clustering [Sil95,SAS94] • fast global illumination • view-independent • High memory consumption due to: • Link structure [WH97] • Hierarchy structure (clusters, subdivided polygons)

5. Hierarchical radiosity • Multi-resolution representation of exchanges • Clustering 

6. Three hierarchy traversals • Refinement : • build the hierarchy • create link: store information of the exchanges • Gather : • compute irradiance due to links Ii =links on iFijBkj • Push-Pull • Descend hierarchy : sum the irradiances • Leaves: reflect entire irradiance • Returning : update the hierarchical representation

7. Hierarchical radiosity B = 0 1 B = E B = 0 2

8. Refinement

9. Gather I1= F1*E I += I1

10. Push-Pull : Descend I2 I1 += I2 I1

11. Push-Pull : Return B1=r1*I1 B = (SAiBi)/A

12. Previous work • Progressive refinement [CCWG88] • Lack of global error control • Memory used by links • Study by Willmott and Heckbert [WH97] • Getting rid of Links [SSSS98] • unshot radiosity • link cache

13. Shooting Algorithm [SSSS98] • Shooting algorithm DB0 = E (sources) DB0 = 0 (others) • Theoretically the same as standard algorithm • Convergence : global error

14. Shooting algorithm B = 0 DB = 0 1 B = E DB = E B = 0 DB = 0 2

15. 1st Iteration B = 0 DB = 0 1 B = E DB = 0 B = 2* I2 DB =2* I2 2

16. 2nd iteration B = 1* I1 DB = 1* I1 1 B = E DB = 0 B = 2* I2 DB =0 2

17. 3th iteration B = 1* I1 DB = 0 1 B = E DB = 0 B = 2* (I2 +I3) DB =2* I3 2

18. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

19. Goals of New Framework • Reduce both link and hierarchy memory consumption • Low speed penalty • Maintain global representation

20. New Framework for Memory Reduction • Goal • Keep only links needed in next iteration • Algorithm • Unshot radiosity [SSSS98] DB = Bi+1 - Bi • Merge radiosity steps • Traverse Link Hierarchy

21. New Framework for Memory Reduction • Link Hierarchy and Traversal • Allows reduction of stored links • Reduce links creation number of links • Remove links where possible • Merge radiosity steps • Facilitates reduction of memory used by radiosity hierarchy • ...allows us to move links “higher” in the hierarchy • Enables effective memory control mechanism

22. Link hierarchy [DS97] Is the father of • Active link = hierarchy leaf • Passive link = hierarchy node

23. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

24. Link Memory Reduction • Only create links when needed • Merge Refine and Gather • Creation Criterion: • First approach : Don’t store links from sources • More sophisticated approaches possible • Control link creation using a cache mechanism • Predict link utility in future iterations

25. Refine And Gather - schema source create create create gather gather gather create and gather create and gather refine refine • Active link = hierarchy leaf • Passive link = hierarchy node

26. Link reduction summary • New approach reduces memory used by links • No overall control of memory • Now, hierarchy uses most of the memory

27. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

28. Hierarchy storage reduction • Goal: reduce memory during refinement • Otherwise we cannot control overall memory usage • Full recursion on link hierarchy • Refine, Gather and Push Pull method • Hierarchy simplification

29. Refine, Gather And PushPull • We have to do only one PushPull • For each hierarchy element • For each iteration • Receiver refinement • Refine gather and push pull on each child • Source refinement • Refine gather and push pull on last child • Else refine gather • Replace hierarchy on which no links arrive

30. Refine Gather and PushPull source gather refine and push pull refine and push pull gather and push pull gather and push pull create and gather create and gather + push pull • Active link = hierarchy leaf • Passive link = hierarchy node

31. Refine Gather and PushPull source create replacement • Active link = hierarchy leaf • Passive link = hierarchy node

32. Texture replacement + 

33. Hierarchy reduction summary • Advantages • Reduce subdivision due to direct light • But • still a memory peak • Need a mechanism to limit memory

34. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

35. Memory control mechanism • Control link memory • Move links higher in the element hierarchy • Increase texture replacement • “Cache”-like test • Estimate the expected depth of Link hierarchy

36. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

37. Tests scenes Medium hall 4 blocks - 169 K Polygons

38. Tests scenes Simple hall 2 blocks - 65 K polygons complex hall 16 blocks - 676 K polygons

39. Memory used by HRC

40. New algorithm (texture replacement)

41. Cluster reduction

42. Overview • Motivation and Previous Work • New Framework • Reduce memory used by links • Reduce memory used by hierarchy • Memory Control Mechanism • Results • Conclusion

43. Summary • New framework for controlling memory • Using Link Hierarchy • Merge all steps of the radiosity solution • Memory control mechanism • Change => Use memory where it’s needed

44. Future Work • Store part of hierarchy on disk • load only the parts needed in memory • Better representation for simplified clusters • image based rendering • volumetric primitives • multi-resolution geometric simplification • More sophisticated memory control mechanisms • take the memory of hierarchy into account