Miloš Hašan Jaroslav Křivánek Philipp Slusallek Kavita Bala Combining Global and Local Virtual Lights for Detailed Glossy Illumination Tomáš Davidovič Saarland University / DFKI Cornell University Charles University, Prague

Goal: Glossy inter-reflections 2

Indirect glossy highlights from complex geometry Our new approach 3 our approach: 6 minutesreference: 244 minutes

Unbiased methods –(Bidirectional) path tracing [Kajiya 86, Lafortune el al. 93] –Metropolis light transport [Veach and Guibas 97] Biased methods –(Progressive) photon mapping [Jensen 2001, Hachisuka et al. 08/09] –Radiance caching [Křivánek 05] Scalable virtual light methods –Lightcuts [Walter et al. 05/06] –Matrix row-column sampling [Hašan et al. 07/09] Previous work 4

1.Generate VPLs 5 Previous work – VPL rendering 2.Render with VPLs

Previous work – VPL energy loss 6

Replace point lights by spheres [Hašan et al. 2009] Alleviates the energy loss but blurs illumination Previous work – VSLs 7 virtual spherical lights (VSLs)reference blur

Compute the missing energy by path tracing [Kollig and Keller 2004] As slow as path-tracing everything (for glossy) Previous work – Compensation 8 indirect illumination Instant radiosity (VPLs) Path tracing

Specific fast solution for each component Our approach 9 indirect illumination

Solution of the global component Solution of the local component Results 10 Outline

Solving the global component

Light transport over long distances Handled by classic global VPLs Scalable solution: visibility clustering 12 Global (clamped) component local global

13 Review of MRCS Pixels Lights Matrix interpretation indirect illumination

Problem statement = Σ ( 14 Review of MRCS Pixels Lights ) indirect illumination

Solution 15 Review of MRCS Pixels Lights ) Σ ( shadow maps for visibility indirect illumination

16 Visibility Clustering – Motivation Lights

17 Global solution overview Row sampling Global solution (clamped) Global VPL tracing shading Reduced matrix visibility Visibility clustering Render lights with reps visibility

Clustering algorithm –Hierarchical splitting –Minimize the clustering cost L2 error of reduced matrix due to visibility approximation 18 Visibility clustering clusters representatives shading visibility

19 Visibility clustering result Matrix row- column sampling Our visibility clustering 10k shadow maps 10k shading lights 5k shadow maps 200k shading lights

Solving the local component

Localized light transport Less energy Solution: Local VPLs 21 Local (compensating) component local global

Kollig & Keller compensation 22 Review of compensation 3) Contribute Clamped energy global

Our approach 23 Local lights – idea Create local light Contribute to a tile global local

Our approach 24 Local lights – technical solution local from tile pixels Probability density Jitter tiles global local

Our approach 25 Local lights – technical solution One-sample visibility global Clamped energy = 0 Reject local 50-75% 2-4x speedup Key idea: Tile visibility approximation

26 The complete local solution Local solution (compensation) Generate local lights Reject zero contrib Connect to global lights Contribute to a tile

27 The complete local solution Local solution (compensation) Global solution (clamped) Indirect illumination solution Localized transport Less energy Reuse on tiles Long distance transport Most of the energy Visibility clustering

28 CPU/GPU cooperation CPU GPU Generate & cluster global VPL Generate local VPLs Render global VPLsRender local VPLs

Results

30 Tableau shadow maps: global lights: local lights: 5, ,000 55,600,000 VSL: 6 min 16 sec Our: 5 min 43 sec reference: 244 min

31 Tableau VSL: 6 min 16 sec Our: 5 min 43 sec reference: 244 min shadow maps: global lights: local lights: 5, ,000 55,600,000

32 Disney Concert Hall shadow maps: global lights: local lights: 15, ,000 13,500,000 Our: 2 min 44 sec reference: 127 min

33 Disney Concert Hall VSL: 1 min 47 sec Our: 2 min 44 sec reference: 127 min shadow maps: global lights: local lights: 15, ,000 13,500,000

34 Kitchen #1 Our: 4 min 16 sec reference: 3343 min shadow maps: global lights: local lights: 10, ,000 25,100,000

35 Kitchen #1 shadow maps: global lights: local lights: 10, ,000 25,100,000 Our: 4 min 16 sec reference: 3343 min

36 Kitchen #1 shadow maps: global lights: local lights: 10, ,000 25,100,000 VSL: 4 min 24 sec reference: 3343 min Our: 4 min 16 sec

37 Kitchen #2 VSL: 6 min 25 sec Our: 5 min 28 sec reference: 6360 min shadow maps: global lights: local lights: 10, ,000 17,100,000

38 Kitchen #2 shadow maps: global lights: local lights: 10, ,000 17,100,000 VSL: 6 min 25 sec Our: 5 min 28 sec reference: 6360 min

39 Kitchen #2 – limitations Loss of shadow definition Small loss of energy Our: 5 min 28 sec reference: 6360 min

Highly glossy materials with GI Split light transport –Global component –Local component –Specialized methods for each Future work –Explore other solutions for global component –Revisit split criteria (MIS instead of clamping?) 40 Conclusions & Future Work

Acknowledgements Marie Curie Fellowship PIOF-GA NSF CAREER , NSF CPA Intel and Intel VCI Microsoft Autodesk German Research Foundation (Excellence Cluster 'Multimodal Computing and Interaction)

Thank you

43 Kitchen #2 – PPM and SPPM (Stochastic) Progressive Photon Mapping PPM: 26 min 40 sec Our: 5 min 28 sec SPPM: 27 min 49 sec