Fast Inverse Reflector Design FIRD

1. Fast Inverse Reflector DesignFIRD Albert Mas Ignacio MartínGustavo Patow Graphics Group of Girona Institut d’Informàtica i Aplicacions Universitat de Girona Spain CEIG08, Barcelona

2. Introduction • Lighting design GGG – IIiA - UdG

3. Introduction Light source Uniform light distribution Reflector Defines the lighting distribution Specular material Holder Holder Reflector Light source GGG – IIiA - UdG

4. ? Introduction Lighting industry problem: Reflector Design The desired lighting is known, but not the reflector Inverse design Iterative try and test process High manufacturing costs GGG – IIiA - UdG

5. Introduction Inverse reflector design methods Very restricted reflector shapes Demand high computational costs Hours or days to compute a solution Not able to generate complex real world reflectors GGG – IIiA - UdG

6. Introduction Goals : Computation of the best approximation, from a family of possible reflectors, to a given desired illumination distribution. Fast GPU algorithm to calculate the reflected rays on the reflector. Reflector parametrizable basis Parameters optimization Use millions of light rays Use highly complex reflector shapes GGG – IIiA - UdG

7. FIRD Introduction State of the art Reflector lighting Optimization Results Conclusions and future work GGG – IIiA - UdG

8. State of the art Inverse reflector design (I) IGP problem(Survey Patow and Pueyo ’05) Numerical solutions Local Illumination [Caffarelli et al. ‘99] Paraboloid intersection One bounce NURBS-based Inverse Reflector Design [Anson et al. ’08] Global Illumination [Patow et al. ‘07] GGG – IIiA - UdG

9. State of the art Inverse reflector design (II) Global illumination [Patow et al. ‘07] Polygonal meshes More than one bounce inside reflector are considered Global optimization method Disadvantages High computational cost Reflector mesh resolution Number of traced rays GGG – IIiA - UdG

10. State of the art GPU Ray Tracing (I) Global illumination method needs a large set of light source rays High computational cost Acceleration : GPU GPU has limitations GPU fits well in our problem GGG – IIiA - UdG

11. State of the art GPU Ray Tracing (III) Ray Engine [Carr et al. ‘02] Environment Mapping [Umenhoffer et al. ‘07] GGG – IIiA - UdG

12. State of the art Y X Z GPU Ray Tracing (II) Reflector made by pressing procedure Height-map Relief Texture Mapping [Oliveira et al. ’00] Simplified Ray-Tracing model GGG – IIiA - UdG

13. State of the art GPU Ray Tracing (IV) Space partitioning methods: Hierarchical subdivision structures Octree Traversal (CPU) [Revelles et al. ‘00] Displacement Mapping [Szirmay-Kalos and Umenhoffer ‘08] Quadtree Relief Mapping [Schroders and Gulik ‘06] Quadtree represented into the GPU as a mip-map texture

14. FIRD Introduction State of the art Reflector lighting Optimization Results Conclusions and future work GGG – IIiA - UdG

15. Reflector Lighting Overview Preprocessing of the input data Reflector geometry transformation Reflections computation Comparison to desired distribution GGG – IIiA - UdG

16. Reflector Lighting Input data Far-field IES Reflector holder Light source Light distribution Position Shape GGG – IIiA - UdG

17. Reflector Lighting Reflector geometry transformation Hierarchical height field representation of the reflector Quadtree Mip-map texture Store normal vectors into a texture GGG – IIiA - UdG

18. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping [Schroders and Gulik ‘06] Intersection search going down the hierarchy GGG – IIiA - UdG

19. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

20. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

21. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

22. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

23. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

24. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

25. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

26. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy GGG – IIiA - UdG

27. Reflector Lighting Reflections computation (I) Quadtree Relief Mapping Intersection search going down the hierarchy

28. Reflector Lighting Reflections computation (II) Quadtree Relief Mapping drawback: recursive reflections Intersection search going up the hierarchy GGG – IIiA - UdG

29. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

30. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

31. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

32. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

33. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

34. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

35. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

36. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

37. Reflector Lighting Reflections computation (II) Intersection search going up the hierarchy GGG – IIiA - UdG

38. Reflector Lighting Comparision to desired distribution (I) Comparison of far-fields Light distribution classification: Histogram on GPU GGG – IIiA - UdG

39. Reflector Lighting Comparision to desired distribution (II) Histogram comparision on GPU Distance metric: l2 GPU Reduction algorithm GGG – IIiA - UdG

40. FIRD Introduction State of the art Reflector lighting Optimization Results Conclusions and future work GGG – IIiA - UdG

41. Optimization Optimization Based on [Patow et al. ‘05] Optimize the parameters used in the parametric reflector shape definition Standard optimization method Brute Force Linear steps Test all possible reflectors Regenerate the height field texture for each reflector Iterative process Stops when difference value is under a given threshold Test all possibilities and take the best one (less difference) GGG – IIiA - UdG

42. FIRD Introduction State of the art Reflector lighting Optimization Results Conclusions and future work GGG – IIiA - UdG

43. Results 30 -30 0 -20 Model A (I) 107 rays GGG – IIiA - UdG

44. Results Model A (II) GGG – IIiA - UdG

45. Results -10 0 0 -7 Model B (I) 5x106 rays GGG – IIiA - UdG

46. Results Model B (II) GGG – IIiA - UdG

47. Results 0 10 0 -6 Model C (I) 107 rays GGG – IIiA - UdG

48. Results Model C (II) GGG – IIiA - UdG

49. Results Results NVidia GeForce 8800 GTX (milliseconds) Mip-map texture size: 1200x800 -> 9 levels GGG – IIiA - UdG

50. FIRD Introduction State of the art Reflector lighting Optimization Results Conclusions and future work GGG – IIiA - UdG