combining global and local virtual lights for detailed glossy illumination

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

4

• 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

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

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)

17

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

18

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

22

Review of compensation

3) Contribute

Clamped

energy

2) Connect

1) Shoot path

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

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

26

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