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Global illumination using local linear density estimation

Editor: Holly Rushmeier Authors:

Philip M. Hubbard,

Donald P. GreenbergAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 16, Issue 3

Pages 217 - 259

https://doi.org/10.1145/256157.256158

Published: 01 July 1997 Publication History

Abstract

This article presents the density estimation framework for generating view-independent global illumination solutions. It works by probabilistically simulating the light flow in an environment with light particles that trace random walks origination at luminaires and then using statistical density estimation techniques to reconstruct the lighting on each surface. By splitting the computation into separate transport and reconstruction stages, we gain many advantages including reduced memory usage, the ability to simulate nondiffuse transport, and natural parallelism. Solutions to several theoretical and practical difficulties in implementing this framework are also described. Light sources that vary spectrally and directionally are integrated into a spectral particle tracer using nonuniform rejection. A new local linear density estimation technique eliminates boundary bias and extends to arbitrary polygons. A mesh decimation algorithm with perceptual calibration is introduced to simplify the Gouraud-shaded representation of the solution for interactive display.

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Index Terms

Global illumination using local linear density estimation
1. Computing methodologies
  1. Computer graphics
  2. Modeling and simulation
    1. Simulation types and techniques

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 16, Issue 3

July 1997

141 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/256157

Editor:
Holly Rushmeier
IBM Corp., Yorktown Heights, NY

Issue’s Table of Contents

Copyright © 1997 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1997

Published in TOG Volume 16, Issue 3

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Cited By

Qiu JYin ZCheng MRen B(2024)NeRC: Rendering Planar Caustics by Learning Implicit Neural RepresentationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325938230:7(4339-4348)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3259382
Jensen HChristensen P(2023)Efficient Simulation of Light Transport in Scenes with Participating Media Using Photon MapsSeminal Graphics Papers: Pushing the Boundaries, Volume 210.1145/3596711.3596745(301-310)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3596711.3596745
Rath AYazici ÖSlusallek P(2023)Focal Path Guiding for Light Transport SimulationACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591543(1-10)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3588432.3591543
Bu FKang XYan DWu RSun HAn JWang X(2023)Acceleration of state–space method based on parallelization for enhancing building thermal process simulation efficiencyEnergy and Buildings10.1016/j.enbuild.2023.113600299(113600)Online publication date: Nov-2023
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Zhu SXu ZJensen HSu HRamamoorthi R(2020)Deep Kernel Density Estimation for Photon MappingComputer Graphics Forum10.1111/cgf.1405239:4(35-45)Online publication date: 20-Jul-2020
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Kang CWang LXu YMeng XSong Y(2016)Adaptive Photon Mapping Based on GradientJournal of Computer Science and Technology10.1007/s11390-016-1622-x31:1(217-224)Online publication date: 8-Jan-2016
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Lessig CDesbrun MFiume E(2014)A constructive theory of sampling for image synthesis using reproducing Kernel basesACM Transactions on Graphics10.1145/2601097.260114933:4(1-14)Online publication date: 27-Jul-2014
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Garcia Hernandez RUrea CPoch JSbert M(2014)Overestimation and Underestimation Biases in Photon Mapping with Non-Constant KernelsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2014.231466520:10(1441-1450)Online publication date: 1-Oct-2014
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