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Approximate and probabilistic algorithms for shading and rendering structured particle systems

Published: 01 July 1985 Publication History

Abstract

Detail enhances the visual richness and realism of computer-generated images. Our stochastic modelling approach, called particle systems, builds complex pictures from sets of simple, volume-filling primitives. For example, structured particle systems have been used to generate trees and a grass-covered forest floor. Particle systems can produce so much irregular, three-dimensional detail that exact shading and visible surface calculations become infeasible. We describe approximate and probabilistic algorithms for shading and the visible surface problem. Because particle systems algorithms generate richly-detailed images, it is hard to detect any deviation from an exact rendering. Recent work in stochastic modelling also enables us to model complex motions with random variation, such as a field of grass blowing in the breeze. We analyze the performance of our current algorithms to understand the costs of our stochastic modelling approach.

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1985
Published in SIGGRAPH Volume 19, Issue 3

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Author Tags

  1. approximation
  2. stochastic modelling

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  • (2024)Parametrization of biological assumptions to simulate growth of tree branching architecturesTree Physiology10.1093/treephys/tpae04544:5Online publication date: 2-May-2024
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