Miloš Hašan

Jaroslav Křivánek

Philipp Slusallek

Kavita Bala

Combining Global and Local Virtual Lights for

Detailed Glossy Illumination

Tomáš Davidovi

čSaarland

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 minutes reference: 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

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• Instant radiosity [Keller 1997]

• Approximate indirect illumination byVirtual Point Lights (VPLs)

1. Generate VPLs

5

Previous work – VPL rendering

2. Render with VPLs

Previous work – VPL energy loss

energy loss material change

[Křivánek et al. 10]

VPLs w/ clamping

GI reference

artifacts

VPL

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• 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

Compensation

ClampingInstantradiosity (VPLs)

Path tracing

• Specific fast solution for each component

Our approach

9

indirect illumination

Compensation

Clamping Global componentVisibility clust.

Local componentLocal VPLs

• 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 MRCSPixe

lsLights• Matrix interpretation

indirect illumination

• Problem statement

= Σ (

14

Review of MRCSPixe

lsLights

)indirect illumination

• Solution

15

Review of MRCSPixe

lsLights

)≈ Σ (

shadow maps for visibility

indirect illumination

• Many VPLs neededfor shading– Shading is cheap

shade from all VPLs

• Cannot afford visibility for every VPL

• Key idea:Separate shading from visibility

16

Visibility Clustering – MotivationLights

shading (all VPLs)

visibility (representatives)

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Global solution overviewRow

sampling

Global solution (clamped)

Global VPL tracing

shading

Reduced matrix

visibility

Visibility clustering

Render lights withreps’ visibility

• Clustering algorithm– Hierarchical splitting– Minimize the clustering cost

• L2 error of reduced matrix due to visibility approximation

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Visibility clustering

clusters

representatives

shading

visibility

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Visibility clustering resultMatrix 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

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Review of compensation

3) Contribute

Clamped

energy

2) Connect

1) Shoot path

global

• Our approach

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Local lights – idea

Create local light

Contribute to a tile

global

local

• Our approach

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Local lights – technical solution

local

from tile pixels

Probability density

Jittertiles

global

local

• Our approach

25

Local lights – technical solution

One-samplevisibility

global

Clampedenergy = 0

Reject

local

50-75%2-4x speedup

• Key idea: Tile visibility approximation

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The complete local solution

Local solution(compensation)

Generate local lights

Reject zero contrib

Connect to global lights

Contributeto a tile

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The complete local solution

Local solution(compensation)

Global solution (clamped)

Indirect illuminationsolution

• Localized transport• Less energy• Reuse on tiles

• Long distance transport

• Most of the energy• Visibility clustering

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CPU/GPU cooperation

CPU

GPU

Generate & cluster globalVPL

Generate local VPLs

Render global VPLs

Render local VPLs

Results

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Tableau

• shadow maps:

• global lights:

• local lights:

5,000

200,000

55,600,000

VSL: 6 min 16 sec

Our: 5 min 43 sec

reference: 244 min

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TableauVSL: 6 min 16 sec

Our: 5 min 43 sec

reference: 244 min

• shadow maps:

• global lights:

• local lights:

5,000

200,000

55,600,000

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Disney Concert Hall

• shadow maps:

• global lights:

• local lights:

15,000

200,000

13,500,000

Our: 2 min 44 sec

reference: 127 min

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Disney Concert HallVSL: 1 min 47 sec

Our: 2 min 44 sec

reference: 127 min

• shadow maps:

• global lights:

• local lights:

15,000

200,000

13,500,000

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Kitchen #1

Our: 4 min 16 sec

reference: 3343 min

• shadow maps:

• global lights:

• local lights:

10,000

200,000

25,100,000

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Kitchen #1

• shadow maps:

• global lights:

• local lights:

10,000

200,000

25,100,000

Our: 4 min 16 sec

reference: 3343 min

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Kitchen #1

• shadow maps:

• global lights:

• local lights:

10,000

200,000

25,100,000

VSL: 4 min 24 sec

reference: 3343 min

Our: 4 min 16 sec

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Kitchen #2VSL: 6 min 25 sec

Our: 5 min 28 sec

reference: 6360 min

• shadow maps:

• global lights:

• local lights:

10,000

300,000

17,100,000

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Kitchen #2

• shadow maps:

• global lights:

• local lights:

10,000

300,000

17,100,000

VSL: 6 min 25 sec

Our: 5 min 28 sec

reference: 6360 min

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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-2008-221716

• NSF CAREER 0644175, NSF CPA 0811680

• Intel and Intel VCI• Microsoft• Autodesk• German Research Foundation

(Excellence Cluster 'Multimodal Computing and Interaction‘)

Thank you

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Kitchen #2 – PPM and SPPM

• (Stochastic) Progressive Photon Mapping

PPM: 26 min 40 sec

Our: 5 min 28 sec

SPPM: 27 min 49 sec