Autonomous lighting agents in global illumination
type: Dataset
description: In computer graphics, physically-based global illumination algorithms such as photon-mapping have a linear progression between complexity and quality. To a given quality, rendering time scale linearly with computer performances. With Moore's law call in question and increasing demand in quality thoose algorithms need more and more optimisations. Existing optimisations such as irradiance caching are themselves very linear in performance gain. We propose a new optimisation method for the photon-mapping global illumination algorithm. We used structures borrowed from Artificial Intelligence such as autonomous agents to reduce computing time of the photon casting phase and rendering phase of the algorithm. Our structure called Autonomous Lighting Agents starts by using an agent-based scene discovery algorithm and is later used to make decisions during rendering inducing less photons beeing casted and smaller rendering times.
issued: 2026-05-23T18:03:50.494068
language: en
modified: 2026-05-23T18:03:50.494073
contact point: Organization
  fn: CCSD
keyword: infoeu-reposemanticsconferenceobject
keyword: infoinfo-grcomputer-science-csgraphics-csgr
keyword: poster-communications
landing page: SIGGRAPH 2009

Autonomous lighting agents in global illumination
type: Distribution
format: HTML
issued: 2026-05-23T18:03:50.497129
modified: 2026-05-23T18:03:50.486132
access URL: https://hal.science/hal-00681787

test_moissonnage_selune
type: Agent