Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination

Transcription

1 Mixing Monte Carlo and Progressive Rendering for Improved Global Illumination Ian C. Doidge Mark W. Jones Benjamin Mora Swansea University, Wales Thursday 14 th June Computer Graphics International 2012

2 ? Motivation X Remove caustics MC Path tracing Add 16x samples 128 filtered samples 128 samples 2048 samples 2

3 Monte Carlo Integration Path Tracing (PT) [Kajiya 86] Bidirectional Path Tracing Path Tracing Iterate [Lafortune & Willems 93] [Veach & Guibas 94] Metropolis Light Transport Modify existing paths [Veach & Guibas 97] 3

4 Monte Carlo Integration Path Tracing (PT) [Kajiya 86] Bidirectional Path Tracing Bidirectional PT Iterate [Lafortune & Willems 93] [Veach & Guibas 94] Metropolis Light Transport Modify existing paths [Veach & Guibas 97] 4

5 Monte Carlo Integration Path Tracing (PT) [Kajiya 86] Bidirectional Path Tracing Metropolis Iterate [Lafortune & Willems 93] [Veach & Guibas 94] Metropolis Light Transport Modify existing paths [Veach & Guibas 97] 5

6 Monte Carlo Integration Path Tracing Good for diffuse lighting Sample stratification Explicit camera/light source connections Poor caustic evaluation Low probability, high luminance paths Result in spiked noise: Converged Reference (PT)

7 Monte Carlo Integration Path Tracing Path Tracing (PT): [Kajiya 86] Bidirectional Path Tracing (BDPT): [Lafortune & Willems 93] [Veach & Guibas 94] Metropolis Light Transport (MLT): Modify existing paths [Veach & Guibas 97] Converged Reference (PT)

8 Monte Carlo Integration Good for diffuse lighting Sample stratification Explicit camera/light source connections Poor caustic evaluation Path Tracing Low probability, high luminance paths Result in spiked noise: Iterate 8

9 Density Estimation SPPM Much better for caustic lighting High photon density regions Inefficient for diffuse lighting Diffuse bounces lead to poor distributions Too few photons in density estimates Photon Mapping (PM): [Jensen 96] Stochastic Progressive PM (SPPM): [Hachisuka 09] Converged Reference (PT)

10 Stochastic Progressive Photon Mapping (SPPM) Ray Tracing Pass Photon Pass Update & Iterate 10

11 Contributions Path Tracing Filter path space and evaluate independently... Efficiently combine MC with density estimation On the fly path filtering Superior RMSE convergence (vs. PT and SPPM) Related Work Our Method Progressive Point-Light-Based Global Illumination [Dammertz et al. 2010] Density Based Outlier Rejection [DeCoro et al. 2010] SPPM 11

12 Algorithm Overview Trace Camera Paths Filter path vertices Compute vertex contributions Diffuse buffer Generate caustic photons Output image Primary hitpoints Gather photons Caustic buffer Iterate 12

13 Path Filtering Separate path space Diffuse and Caustic lighting Based on path/surface interactions Two disjoint sub-spaces Account for all possible light transport 13

14 D Diffuse interaction S Specular interaction L Implicit lighting (L) Explicit lighting Apply pattern matching Path Filtering Diffuse subspace: Caustic subspace: X 14

15 Filtered Path Tracing X X Path Tracing Iterate Filtered Path Tracing Iterate 15

16 D Diffuse interaction S Specular interaction L Implicit lighting (L) Explicit lighting Filtering Example E D D D D S D Binary Path Encoding: D D D S D D E Diffuse subspace: Vertex Interactions 16

17 D Diffuse interaction S Specular interaction L Implicit lighting (L) Explicit lighting Filtering Example E (L) (L) X (L) (L) D D D D S D Binary Path Encoding: D D D S D D E Diffuse subspace: Perform table lookup Implicit Lighting Explicit Lighting 17

18 D Diffuse interaction S Specular interaction L Implicit lighting (L) Explicit lighting Filtering Example E (L) (L) X (L) (L) D D D D S D X 18

19 Overview Trace Camera Paths Filter path vertices Compute vertex contributions Diffuse buffer Generate caustic photons Output image Primary hitpoints Gather photons Caustic buffer Iterate 19

20 Filtered SPPM X Primary Hitpoints X Photon Pass Update 20

21 Photon Filtering L D X Caustic subspace: D L S D D D Deposit only caustic photons: Lower memory cost (~10%) Faster photon gathering Similar caustic convergence 21

22 Image Reconstruction Diffuse Buffer Caustic Buffer Full Global Illumination Combine diffuse and caustic buffers: Pixel-wise addition Obtain intermediate results after each iteration 22

23 Path Tracing Results SPPM Equal time comparison after 30 minutes 23

24 Path Tracing Results SPPM Equal time comparison after 30 minutes 24

25 Results SPPM Path Tracing Converged PT Our Method Equal time comparison after 5 minutes 25

26 Results Path Tracing Our Method SPPM Equal time comparison after 5 minutes 26

27 Error comparison (RMSE) 27

28 Conclusion Efficiently render diffuse and caustic lighting: Progressive multi-pass method Diffuse lighting using Monte Carlo integration Caustic lighting via reduced SPPM No additional bias Future Work Automatic adjustment of camera paths vs. photons Adaptive photon generation Improve efficiency and photon distribution 28

29 Thank you for listening! Questions? (and Answers) 29

30 Thank you for listening! Questions? (and Answers) 30

31 31

32 Error comparison (RMSE)

33 Error comparison (RMSE)

34 Error comparison (RMSE)

35 Motivation Path Tracing Our Method Converged Reference (PT) SPPM 35

36 Overview Trace Camera Paths Filter path vertices Compute vertex contributions Diffuse buffer Generate caustic photons Output image Primary hit points Gather photons Caustic buffer Iterate

37 Caustic only SPPM X X X Filtered Path Tracing Pass X Filtered Photon Pass Iterate

38 Filtered SPPM X X Filtered Photon Pass Iterate

39 Diffuse photon Caustic photon Our Method Photon Filtering SPPM Caustic subspace: Deposit only caustic photons: Lower memory cost Faster photon gathering Same rate of convergence

40 Stochastic Progressive Photon Mapping (SPPM) Ray Tracing Pass Photon Pass Iterate

41 The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. Path Filtering Direct lighting: ES*DL Multiple Bounce Diffuse: ES*DDD+L Single Bounce Diffuse: ES*DDL Caustic Lighting: E(S D)*D+S*L E(S D)*D+S*DL

42 Monte Carlo Integration Path Tracing Iterate Bidirectional Path Tracing Iterate 42

43 Stochastic Progressive Photon Mapping (SPPM) Ray Tracing Pass Photon Pass Iterate

44 Monte Carlo Integration Path Tracing Monte Carlo Integration methods: Good for diffuse lighting Sample stratification Explicit camera/light source connections Poor caustic evaluation Low probability, high luminance paths Result in spiked noise: Converged Reference (PT)

45 Density Estimation SPPM Density Estimation methods: Much better for caustic lighting Higher photon density regions Can handle difficult specular paths Inefficient for diffuse lighting Diffuse bounces lead to poor photon distribution Insufficient photons in density estimates Converged Reference (PT)

46 Overview Multi-pass progressive algorithm: Path Tracing for diffuse lighting (No bias) SPPM for 'caustic' lighting (Progressively decreasing bias) Filter path space and evaluate independently... Efficiently combine MC and density estimation methods On the fly path filtering Superior RMSE convergence (vs. PT and SPPM) Related Work Density-based outlier rejection [Decoro et al. 2010] Progressive point-light based global illumination [Dammertz et al. 2010] Caustic Forecasting [Budge et al. 2008]

47 The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location. Progressive Photon Mapping Ray Tracing Pass Photon Pass Iterate

48 Related Work Density-based outlier rejection [Decoro et al. 2010] Progressive point-light based global illumination [Dammertz et al. 2010] Caustic Forecasting [Budge et al. 2008]