1. Hokkaido University Efficient Rendering of Lightning Taking into Account Scattering Effects due to Clouds and Atmospheric Particles Tsuyoshi Yamamoto Tomoyuki Nishita (The University of Tokyo) Yoshinori Dobashi (Hokkaido University)

2. Hokkaido University Overview Introduction Effects of Atmospheric Scattering due to Lightning Clouds Illuminated by Lightning Results Conclusions

3. Hokkaido University Introduction Photo-realistic Rendering of Natural Scenes - visual assessments - flight simulators - visual assessments - flight simulators Previous methods: Clear/cloudy days Simulations under bad weather conditions - windstorm, sandstorm, rain, lightning - Realistic rendering of scenes including lightning - Development of efficient rendering method Purposes

4. Hokkaido University Important Elements Shape of lightning Illuminating clouds Atmospheric scattering Illuminating ground

5. Hokkaido University Previous Methods Methods related to lightning Modeling using particle systems [Reed94] Probabilistic modeling [Kruszewski99] DLA taking into account clouds [Sosorbaram01] clouds atmospheric scattering rendering speed - - - - - slow fast slow

6. Hokkaido University Previous Methods Modeling using particle systems [Reed94] Probabilistic modeling [Kruszewski99] DLA taking into account clouds [Sosorbaram01] clouds atmospheric scattering rendering speed - - - - - slow fast slow Methods related to lightning

7. Hokkaido University Previous Methods Modeling using particle systems [Reed94] Probabilistic modeling [Kruszewski99] DLA taking into account clouds [Sosorbaram01] clouds atmospheric scattering rendering speed - - - - - slow fast slow Methods related to lightning

8. Hokkaido University Previous Methods Modeling using particle systems [Reed94] Probabilistic modeling [Kruszewski99] DLA taking into account clouds [Sosorbaram01] clouds atmospheric scattering rendering speed - - - - - slow fast slow Methods related to lightning fast Our method

9. Hokkaido University hardware rendering of smokes shafts of light through trees and clouds [Max86] [Dobashi00] hardware rendering of shafts of light through clouds [Jansen98] photon map [Rushmeier87] extending radiosity method [Nishita87] shafts of light produced by spotlights [Stam99, Stam01] Previous Methods Related to clouds and atmospheric scattering None of these takes into account scattering effects due to lightning flash.

10. Hokkaido University Use of Reed‘s method for modeling User specifies Color of lightning Atmospheric scattering due to flash of lightning Clouds illuminated by flash of lightning Efficient rendering of clouds for fly- through animations Features of Proposed Method Use of Reed‘s method for modeling User specifies Color of lightning Atmospheric scattering due to flash of lightning Clouds illuminated by flash of lightning Efficient rendering of clouds for fly- through animations

11. Hokkaido University Use of Reed‘s method for modeling User specifies Color of lightning Atmospheric scattering due to flash of lightning Clouds illuminated by flash of lightning Efficient rendering of clouds for fly- through animations Features of Proposed Method

12. Hokkaido University Overview Introduction Effects of Atmospheric Scattering due to Lightning Clouds Illuminated by Lightning Results Conclusions

13. Hokkaido University Atmospheric Scattering viewpoint clouds lightning point sources Placing point light sources

14. Hokkaido University viewpoint V P Consider a single source k s t IkIk  dtI s ts FI k aa a k eye )( ))(exp( )(cos)( 2 0 )(        a  : density F : phase function  : phase angle  a  : extinction coefficient s : distance between source and P t : distance between V and P I k : intensity of point source  : wavelength Atmospheric Scattering Placing point light sources

15. Hokkaido University viewpoint P s t IkIk  dtI s ts FI k aa a k eye )( ))(exp( )(cos)( 2 0 )(       No analytical solutions Atmospheric Scattering Consider a single source k Placing point light sources

16. Hokkaido University dtI s ts FI k aa a k eye )( ))(exp( )(cos)( 2 0 )(       No analytical solutions Atmospheric Scattering Consider a single source k Placing point light sources viewpoint IkIk I eye (k) Ray tracing Computationally expensive Use of look-up table

17. Hokkaido University Efficient Computation Using Look-up Table Creating look-up table Intensity due to a single source dtI s ts FI k aa a k eye )( ))(exp( )(cos)( 2 0 )(       s t  (u eye, v eye ) P (u, v) - local coordinate uv ),,()( )( eye lk k vuIII  22 vus  || uut  22 /1cos eye vu  du vu uuvu vu FvuI eye aa u a l 22 22 22 |))|(exp( ) 1 (),,(          u v

18. Hokkaido University function of (u eye, v eye, ) preparing table by changing values of (u eye, v eye, ) -T < (u eye, v eye ) < T ( T: specified by user) : sampled at R, G, B Efficient Computation Using Look-up Table s t  (u eye, v eye ) P (u, v) u v ),,()( )( eye lk k vuIII 

19. Hokkaido University Efficient Computation Using Look-up Table    n k kpkplkp lvuIII 1,, ),,()( Intensity of pixel Can be computed efficiently using look-up table function of (u eye, v eye, ) preparing table by changing values of (u eye, v eye, ) s t  (u eye, v eye ) P (u, v) u v ),,()( )( eye lk k vuIII 

20. Hokkaido University Overview Introduction Effects of Atmospheric Scattering due to Lightning Clouds Illuminated by Lightning Results Conclusions

21. Hokkaido University - voxels Intensity Calculation of Clouds Density distribution - metaballs [Dobashi00] metaballs R q effective radius center density metaball field function

22. Hokkaido University point sources lightning - voxels Intensity Calculation of Clouds Density distribution - metaballs [Dobashi00] metaball - use of LOD - use of hardware Intensity Calculation - sum of intensity due to each point source

23. Hokkaido University point sources lightning - 3D voxels Intensity Calculation of Clouds Density distribution - metaballs [Dobashi00] metaball - use of LOD - use of hardware Intensity Calculation - sum of intensity due to each point source

24. Hokkaido University Computing Attenuation Using Hardware Attenuation to each metaball - use of hardware-accelerated splatting [Dobashi00] - limited to parallel sources - limited to parallel sources - extending to point sources - extending to point sources metaball lightning point source k

25. Hokkaido University box as six screens metaball - placing a box as 6 screens Computing Attenuation Using Hardware Attenuation to each metaball point source k

26. Hokkaido University box as six screens metaball - placing a box as 6 screens Computing Attenuation Using Hardware Attenuation to each metaball point source k

27. Hokkaido University box as six screens - place billboards at centers of metaballs billboard (square polygon) - project metaballs - pixel value of the centers attenuation ratio Problem cost ∝ no. of metaballs (realistic clouds ： tens of thousands of metaballs) Using LOD ： grouping metaballs hierarchically - placing a box as 6 screens Computing Attenuation Using Hardware Attenuation to each metaball point source k

28. Hokkaido University Efficient Computation Using LOD metaball j IkIk I kj r Light reaching metaball ： - inversely proportional to square of distance point source k 2 2 )) ( ( exp( ) ) ( ( ) ) ( ( r r r r I I I I k k kj      

29. Hokkaido University - attenuation due to cloud particles metaball j IkIk I kj r point source k Efficient Computation Using LOD Light reaching metaball ： - inversely proportional to square of distance 2 2 )) ( ( exp( ) ) ( ( ) ) ( ( r r r r I I I I k k kj      

30. Hokkaido University metaball - intensity is small at distant regions, and almost uniform. Efficient Computation Using LOD Light reaching metaball ： point source k - attenuation due to cloud particles - inversely proportional to square of distance 2 2 )) ( ( exp( ) ) ( ( ) ) ( ( r r r r I I I I k k kj      

31. Hokkaido University metaball Approximation by larger metaballs Selecting appropriate metaballs depending on distances Efficient Computation Using LOD Light reaching metaball ： point source k - intensity is small at distant regions, and almost uniform. - attenuation due to cloud particles - inversely proportional to square of distance 2 2 )) ( ( exp( ) ) ( ( ) ) ( ( r r r r I I I I k k kj      

32. Hokkaido University Representing metaballs using octree Selecting appropriate levels depending on distances metaball larger metaball Grouping neighboring metaballs - density ： average - radius ： twice - density ： average - radius ： twice Efficient Computation Using LOD

33. Hokkaido University Selection of appropriate levels - energy received by metaball j - condition ： (  : threshold) metaball j point source k IkIk I kj r E j = (light reaching metaball) x (volume) dV j Efficient Computation Using LOD dV j 2 ))(exp()( r rI k        2 ))(exp()(max r rI k dV j requires integration of density of cloud particles

34. Hokkaido University ∴ exp(-  (r)) < 1.0 ()   2 )(max r I k dV j  Selection of appropriate levels - energy received by metaball j metaball j point source k IkIk I kj r dV j Efficient Computation Using LOD - condition ： energy when there are no particles between metaball and point source.     2 ))(exp()(max r rI k dV j E j = (light reaching metaball) x (volume) dV j 2 ))(exp()( r rI k  

35. Hokkaido University clouds point source k   2 )}(max{ r I k dV j - condition ： - check metaballs of highest level Selection of appropriate levels Efficient Computation Using LOD

36. Hokkaido University - proceed to metaballs of lower levels ○ ×   2 )}(max{ r I k dV j - condition ： - check metaballs of highest level Selection of appropriate levels Efficient Computation Using LOD clouds point source k

37. Hokkaido University × ○ - proceed to metaballs of lower levels   2 )}(max{ r I k dV j - condition ： - check metaballs of highest level Selection of appropriate levels Efficient Computation Using LOD clouds point source k

38. Hokkaido University Reducing number of metaballs to be processed - proceed to metaballs of lower levels   2 )}(max{ r I k dV j - condition ： - check metaballs of highest level Selection of appropriate levels Efficient Computation Using LOD clouds point source k selected metaballs

39. Hokkaido University Overview Introduction Features of Our method Effects of Atmospheric Scattering due to Lightning Clouds Illuminated by Lightning Results Conclusions

40. Hokkaido University Results Verification using simple example density of atmospheric particles ： 0.15 Parameter settings: attenuation ratio ： 0.03 threshold  ： 0.2 no. of point sources ： 50 table size ： 128x128 (T: 1.5 [km]) no. of metaballs: 250,000

41. Hokkaido University Results 50 times faster! with LODwithout LOD Computation time - with LOD : 8 [s] - without LOD : 400 [s] computer ： PentiumIII (733MHz), GeForce2GTS Image size: 720X480

42. Hokkaido University Results (a) lightning in clouds(b) multiple lightning (c) colored lightning (pink)(d) lightning at sunset

43. Hokkaido University Example Animation （ VIDEO) On animating lightning: Simulation of lightning under different conditions Flight simulation - Initial points are determined randomly in clouds. - Periods from occurrence to the extinction are determined randomly, less than 0.5 seconds. - Intensity is determined randomly.

44. Hokkaido University Conclusions Realistic image synthesis of scenes including lightning - atmospheric scattering due to flash of lightning - clouds illuminated by flash of lightning - efficient rendering using look-up table and idea of LOD - hierarchical imposters for efficient rendering of clouds

45. Hokkaido University Future Work Further speeding up for real-time simulations Automatic determination of parameters Automatic determination of lightning color

46. Hokkaido University Basic Idea Atmospheric scattering Intensity of clouds viewpoint clouds point light sources lightning - use of look-up table - use of idea of level of detail (LOD)

47. Hokkaido University Basic Idea Atmospheric scattering Intensity of clouds Efficient rendering of clouds clouds viewpoint Imposter textures - use of look-up table - use of idea of level of detail (LOD) - use of imposter method

48. Hokkaido University Rendering of Clouds Using Imposters viewpoint clouds (metaball) screen Grouping metaballs Drawing all metaballs - increasing rendering time - use of imposters

49. Hokkaido University Placing transparent polygon for each group Creating textures by drawing metaballs Rendering of Clouds Using Imposters Grouping metaballs Drawing all metaballs - increasing rendering time - use of imposters viewpoint clouds (metaball)

50. Hokkaido University Imposter texture Draw texture-mapped polygons Place transparent polygon for each group Create textures by drawing clouds Rendering of Clouds Using Imposters Grouping metaballs Drawing all metaballs - increasing rendering time - use of imposters viewpoint

51. Hokkaido University viewpoint When viewpoint moves… Rendering of Clouds Using Imposters Imposter texture

52. Hokkaido University Reuse textures - reducing rendering time - regions near viewpoint: - distant regions from viewpoint: Grouping metaballs adaptively finer grouping is needed coarse grouping is sufficient Conditions for maintaining accuracy viewpoint Imposter texture When viewpoint moves… Rendering of Clouds Using Imposters

53. Hokkaido University Adaptive Generation of Imposters Making use of octree for metaballs Selecting appropriate levels depending on distance from viewpoint viewpoint clouds Imposter Rendering clouds without losing accuracy

54. Hokkaido University 7 times faster Results with imposters without imposters Computation time (150 frames) - with imposters : 90 [s] - without imposters : 684 [s] computer ： PentiumIII (733MHz), GeForce2GTS Image size: 720X480

55. Hokkaido University Basic Idea Atmospheric scattering Intensity of clouds viewpoint clouds point light sources lightning - use of look-up table - use of idea of level of detail (LOD)