article

Lightcuts: a scalable approach to illumination

Authors:

Sebastian Fernandez,

Michael Donikian,

Donald P. GreenbergAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 24, Issue 3

Pages 1098 - 1107

https://doi.org/10.1145/1073204.1073318

Published: 01 July 2005 Publication History

Abstract

Lightcuts is a scalable framework for computing realistic illumination. It handles arbitrary geometry, non-diffuse materials, and illumination from a wide variety of sources including point lights, area lights, HDR environment maps, sun/sky models, and indirect illumination. At its core is a new algorithm for accurately approximating illumination from many point lights with a strongly sublinear cost. We show how a group of lights can be cheaply approximated while bounding the maximum approximation error. A binary light tree and perceptual metric are then used to adaptively partition the lights into groups to control the error vs. cost tradeoff.We also introduce reconstruction cuts that exploit spatial coherence to accelerate the generation of anti-aliased images with complex illumination. Results are demonstrated for five complex scenes and show that lightcuts can accurately approximate hundreds of thousands of point lights using only a few hundred shadow rays. Reconstruction cuts can reduce the number of shadow rays to tens.

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Index Terms

Lightcuts: a scalable approach to illumination
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Texturing

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 24, Issue 3

July 2005

826 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1073204

Issue’s Table of Contents

Copyright © 2005 ACM.

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Publication History

Published: 01 July 2005

Published in TOG Volume 24, Issue 3

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Gadia DLombardo VMaggiorini DNatilla A(2024)Implementing Many-Lights Rendering with IES-Based LightsApplied Sciences10.3390/app1403102214:3(1022)Online publication date: 25-Jan-2024
https://doi.org/10.3390/app14031022
Tokuyoshi YIkeda SKulkarni PHarada T(2024)Hierarchical Light Sampling with Accurate Spherical Gaussian LightingSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687647(1-11)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3680528.3687647
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https://dl.acm.org/doi/10.1145/3665320.3670993
Lu HChang WHedstrom TLi T(2024)Real-Time Path Guiding Using Bounding Voxel SamplingACM Transactions on Graphics10.1145/365820343:4(1-14)Online publication date: 19-Jul-2024
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Zheng CHuo YMo SZhong ZWu ZHua WWang RBao H(2023)NeLT: Object-Oriented Neural Light TransferACM Transactions on Graphics10.1145/359649142:5(1-16)Online publication date: 29-Aug-2023
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Misso ZLi YBurley BTeece DJarosz W(2023)Progressive null-tracking for volumetric renderingACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591557(1-10)Online publication date: 23-Jul-2023
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Noh GChoi HMoon B(2023)Enhanced Direct Lighting Using Visibility-Aware Light SamplingAdvances in Computer Graphics10.1007/978-3-031-50072-5_15(187-198)Online publication date: 28-Aug-2023
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