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SURE-based Optimization for Adaptive Sampling and Reconstruction Supplementary Materials Tzu-Mao Li Yu-Ting Wu Yung-Yu Chuang National Taiwan University PART I Equal-Time Comparison Compared Methods: Monte Carlo Greedy Error

  1. SURE-based Optimization for Adaptive Sampling and Reconstruction Supplementary Materials Tzu-Mao Li Yu-Ting Wu Yung-Yu Chuang National Taiwan University

  2. PART I Equal-Time Comparison Compared Methods: • Monte Carlo • Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] • Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] • SURE-based Optimization (our approach, using cross bilateral filters)

  3. SPONZA Global Illumination (Path Tracing) Motion Blur 1600 x 1200

  4. SPONZA Equal-time Monte Carlo , 68 spp, 890.5 sec.

  5. SPONZA Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 63.84 spp, 906.2 sec.

  6. SPONZA Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 16 spp, 1676.1 sec.

  7. SPONZA SURE-based Optimization (Our Approach) , 63.24 spp, 896.0 sec.

  8. SPONZA Reference , 8192 spp

  9. TOWN Environment Lighting Area Lighting Motion Blur 800 x 600

  10. TOWN Equal-time Monte Carlo , 82 spp, 59.9 sec.

  11. TOWN Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 51.82 spp, 61.8 sec.

  12. TOWN Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 272.4 sec.

  13. TOWN SURE-based Optimization (Our Approach) , 39.79 spp, 59.6 sec.

  14. TOWN Reference , 4096 spp

  15. SIBENIK Global Illumination (One-Bounce Path Tracing) Depth of Field 1024 x 1024

  16. SIBENIK Equal-time Monte Carlo , 44 spp, 140.0 sec.

  17. SIBENIK Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 39.86 spp, 135.0 sec.

  18. SIBENIK Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 363.0 sec.

  19. SIBENIK SURE-based Optimization (Our Approach) , 26.69 spp, 140 sec.

  20. SIBENIK Reference , 4096 spp

  21. TEAPOT Environment Lighting Glossy Reflection 800 x 800

  22. TEAPOT Equal-time Monte Carlo , 35 spp, 42.0 sec.

  23. TEAPOT Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 23.96 spp, 44.3 sec.

  24. TEAPOT Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 374.4 sec.

  25. TEAPOT SURE-based Optimization (Our Approach) , 8 spp, 40.4 sec.

  26. TEAPOT Reference , 4096 spp

  27. GARGOYLE Global Illumination (One-Bounce Path Tracing) 1024 x 1024

  28. GARGOYLE Equal-time Monte Carlo , 56 spp, 161.7 sec.

  29. GARGOYLE Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 43.92 spp, 167.4 sec.

  30. GARGOYLE Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 608.3 sec.

  31. GARGOYLE SURE-based Optimization (Our Approach) , 30.90 spp, 160.0 sec.

  32. GARGOYLE Reference , 4096 spp

  33. SANMIGUEL Global Illumination (Path Tracing) 1580 x 986

  34. SANMIGUEL Equal-time Monte Carlo , 70 spp, 1209.4 sec.

  35. SANMIGUEL Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 63.59 spp, 1239.9 sec.

  36. SANMIGUEL Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 16 spp, 2617.9 sec.

  37. SANMIGUEL SURE-based Optimization (Our Approach) , 61.69 spp, 1228.9 sec.

  38. SANMIGUEL Reference , 8192 spp

  39. PART II Equal-Sample Comparison Compared Methods: • Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] • Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] • SURE-based Optimization (our approach, using cross bilateral filters)

  40. SPONZA Global Illumination (Path Tracing) Motion Blur 1600 x 1200

  41. SPONZA Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 16 spp, 210.0 sec.

  42. SPONZA Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 16 spp, 1676.1 sec.

  43. SPONZA SURE-based Optimization (Our Approach) , 16 spp, 273.3 sec.

  44. SPONZA Reference , 8192 spp

  45. TOWN Environment Lighting Area Lighting Motion Blur 800 x 600

  46. TOWN Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 8 spp, 9.4 sec.

  47. TOWN Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 272.4 sec.

  48. TOWN SURE-based Optimization (Our Approach) , 8 spp, 20.0 sec.

  49. TOWN Reference , 4096 spp

  50. SIBENIK Global Illumination (One-Bounce Path Tracing) Depth of Field 1024 x 1024

  51. SIBENIK Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 8 spp, 27.6 sec.

  52. SIBENIK Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 363.0 sec.

  53. SIBENIK SURE-based Optimization (Our Approach) , 8 spp, 64.2 sec.

  54. SIBENIK Reference , 4096 spp

  55. TEAPOT Environment Lighting Glossy Reflection 800 x 800

  56. TEAPOT Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 8 spp, 14.1 sec.

  57. TEAPOT Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 374.4 sec.

  58. TEAPOT SURE-based Optimization (Our Approach) , 8 spp, 40.4 sec.

  59. TEAPOT Reference , 4096 spp

  60. GARGOYLE Global Illumination (One-Bounce Path Tracing) 1024 x 1024

  61. GARGOYLE Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 8 spp, 28.6 sec.

  62. GARGOYLE Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 8 spp, 608.3 sec.

  63. GARGOYLE SURE-based Optimization (Our Approach) , 8 spp, 68.3 sec.

  64. GARGOYLE Reference , 4096 spp

  65. SANMIGUEL Global Illumination (Path Tracing) 1580 x 986

  66. SANMIGUEL Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] , 16 spp, 304.4 sec.

  67. SANMIGUEL Random Parameter Filtering [Sen and Darabi, ACMTOG 2012] , 16 spp, 2617.9 sec.

  68. SANMIGUEL SURE-based Optimization (Our Approach) , 16 spp, 336.3 sec.

  69. SANMIGUEL Reference , 8192 spp

  70. PART III Equal-Time Comparison for Isotropic Gaussian Filters Compared Methods: • Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011] • SURE-based Optimization (our approach, using isotropic Gaussian filters)

  71. TOASTERS Area Lighting Depth of Field 1024 x 1024

  72. TOASTERS Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011]

  73. TOASTERS SURE-based Optimization (Our Approach) , using Isotropic Gaussian Filters

  74. TOASTERS Reference , 4096 spp

  75. TOASTERS – Scale Selection Map Greedy Error Minimization [Rousselle et al., SIGGRAPH Asia 2011]

  76. TOASTERS - Scale Selection Map SURE-based Optimization (Our Approach) , using Isotropic Gaussian Filters

  77. PART IV Equal-Time Comparison for Cross Non-local Means Filters Compared Methods: • Global cross non-local means filters • SURE-based Optimization (our approach, using cross non-local means filters)

  78. TOWN Environment Lighting Area Lighting Motion Blur 800 x 600

  79. TOWN Global Non-local Means Filter , 41.2 spp

  80. TOWN SURE-based Optimization (Our Approach) , using Cross Non-local Means Filters , 41.2 spp, 244.7 sec.

  81. TOWN Reference , 4096 spp

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