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Multidimensional Lightcuts Bruce Walter Adam Arbree Kavita Bala Donald P. Greenberg Program of Computer Graphics, Cornell University.

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Presentation on theme: "Multidimensional Lightcuts Bruce Walter Adam Arbree Kavita Bala Donald P. Greenberg Program of Computer Graphics, Cornell University."— Presentation transcript:

1 Multidimensional Lightcuts Bruce Walter Adam Arbree Kavita Bala Donald P. Greenberg Program of Computer Graphics, Cornell University

2 Problem Simulate complex, expensive phenomena –Complex illumination –Anti-aliasing –Motion blur –Participating media –Depth of field

3 Problem Simulate complex, expensive phenomena –Complex illumination –Anti-aliasing –Motion blur –Participating media –Depth of field

4 Problem Simulate complex, expensive phenomena –Complex illumination –Anti-aliasing –Motion blur –Participating media –Depth of field

5 Problem Complex integrals over multiple dimensions –Requires many samples camera

6 Multidimensional Lightcuts New unified rendering solution –Solves all integrals simultaneously –Accurate –Scalable

7 Related Work Motion blur –Separable [eg, Korein & Badler 83, Catmull 84, Cook 87, Sung 02] –Qualitative [eg, Max & Lerner 85, Wloka & Zeleznik 96, Myszkowski et al. 00, Tawara et al. 04] –Surveys [Sung et al. 02, Damez et al. 03] Volumetric –Approximate [eg, Premoze et al. 04, Sun et al. 05] –Survey [Cerezo et al. 05] Monte Carlo –Photon mapping [Jensen & Christensen 98, Cammarano & Jensen 02] –Bidirectional [Lafortune & Willems 93, Bekaert et al. 02, Havran et al. 03] –Metropolis [Veach & Guibas 97, Pauly et al. 00, Cline et al. 05]

8 Related Work Lightcuts (SIGGRAPH 2005) –Scalable solution for complex illumination –Area lights –Sun/sky –HDR env maps –Indirect illumination Millions of lights  hundreds of shadow rays –But only illumination at a single point

9 Insight Holistic approach –Solve the complete pixel integral –Rather than solving each integral individually

10 Direct only (relative cost 1x)Direct+Indirect (1.3x) Direct+Indirect+Volume (1.8x)Direct+Indirect+Volume+Motion (2.2x)

11 Camera Discretize full integral into 2 point sets –Light points ( L ) –Gather points ( G ) Point Sets Light points

12 Camera Discretize full integral into 2 point sets –Light points ( L ) –Gather points ( G ) Point Sets Light points

13 Camera Discretize full integral into 2 point sets –Light points ( L ) –Gather points ( G ) Point Sets Light points Pixel Gather points

14 Discretize full integral into 2 point sets –Light points ( L ) –Gather points ( G ) Point Sets Light points Gather points

15 Discrete Equation Material term Pixel =  S j M ji G ji V ji I i  ( j,i)   G x L Geometric term Visibility term Light intensity Gather strength Sum over all pairs of gather and light points –Can be billions of pairs per pixel

16 Key Concepts Unified representation –Pairs of gather and light points Hierarchy of clusters –The product graph Adaptive partitioning (cut) –Cluster approximation –Cluster error bounds –Perceptual metric (Weber’s law)

17 Product Graph Explicit hierarchy would be too expensive –Up to billions of pairs per pixel Use implicit hierarchy –Cartesian product of two trees (gather & light)

18 Light tree Gather tree Product Graph L0 L1 L2 L3 L4 L5 L6 G1 G0 G2 L0 L1L2 L3 G0 G1

19 Product Graph Light tree Gather tree X = L0 L1 L2 L3 L4 L5 L6 G1 G0 G2 G1 G0 G2 L0L4L1L6L2L5L3 Product Graph

20 Light tree Gather tree X = L0 L1 L2 L3 L4 L5 L6 G1 G0 G2 G1 G0 G2 L0L4L1L6L2L5L3 Product Graph

21 G1 G0 G2 L0L4L1L6L2L5L3 Product Graph

22 G1 G0 G2 L0L4L1L6L2L5L3 Product Graph

23 Light tree Gather tree X = L0 L1 L2 L3 L4 L5 L6 G1 G0 G2 G1 G0 G2 L0L4L1L6L2L5L3 Product Graph

24 Cluster Representatives

25

26 Collapse cluster-cluster interactions to point-cluster –Minkowski sums –Reuse bounds from Lightcuts Compute maximum over multiple BRDFs –Rasterize into cube-maps More details in the paper Error Bounds

27 Algorithm Summary Once per image –Create lights and light tree For each pixel –Create gather points and gather tree for pixel –Adaptively refine clusters in product graph until all cluster errors < perceptual metric

28 L6 G2 L1L2L3L4L5L0 G1 G0 Start with a coarse cut –Eg, source node of product graph Algorithm Summary

29 L6 G2 L1L2L3L4L5L0 G1 G0 Choose node with largest error bound & refine –In gather or light tree Algorithm Summary

30 L6 G2 L1L2L3L4L5L0 G1 G0 Choose node with largest error bound & refine –In gather or light tree Algorithm Summary

31 L6 G2 L1L2L3L4L5L0 G1 G0 Repeat process Algorithm Summary

32 L6 G2 L1L2L3L4L5L0 G1 G0 Until all clusters errors < perceptual metric –2% of pixel value (Weber’s law) Algorithm Summary

33 Results Limitations –Some types of paths not included Eg, caustics –Prototype only supports diffuse, Phong, and Ward materials and isotropic media

34 Roulette 7,047,430 Pairs per pixel Time 590 secs Avg cut size 174 (0.002%)

35 Roulette

36 Scalability

37

38 Metropolis Comparison Our result Time 9.8min Metropolis Time 148min (15x) Visible noise 5% brighter (caustics etc.) Zoomed insets

39 Kitchen 5,518,900 Pairs per pixel Time 705 secs Avg cut size 936 (0.017%)

40 Kitchen

41 Tableau

42 Temple

43 Conclusions New rendering algorithm –Unified handling of complex effects Motion blur, participating media, depth of field etc. –Product graph Implicit hierarchy over billions of pairs –Scalable & accurate

44 Future Work Other types of paths –Caustics, etc. Bounds for more functions –More materials and media types Better perceptual metrics Adaptive point generation

45 Acknowledgements National Science Foundation grants ACI and CCF Intel corporation for support and equipment The modelers –Kitchen: Jeremiah Fairbanks –Temple: Veronica Sundstedt, Patrick Ledda, and the graphics group at University of Bristol –Stanford and Georgia Tech for Buddha and Horse geometry

46 The End Questions?

47 Multidimensional Lightcuts Bruce Walter Adam Arbree Kavita Bala Donald P. Greenberg Program of Computer Graphics, Cornell University

48 L0 L1 L2 L3 L4L5 L6 G1 G0 G2 Exists at first or second time instant. L0 L1 L2 L3 L2 L1 L2 G1 L1 L0 G0 G1 G0 Light TreeGather Tree Representatives

49 Lightcuts key concepts Unified representation –Convert all lights to points Hierarchy of clusters –The light tree Adaptive cut –Partitions lights into clusters Cut Light Tree Lights

50 180 Gather points X 13,000 Lights = 234,000 Pairs per pixel Avg cut size 447 (0.19%)

51 114,149,280 Pairs per pixel Avg cut size 821 Time 1740 secs

52 Types of Point Lights Omni –Spherical lights Oriented –Area lights, indirect lights Directional –HDR env maps, sun&sky


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