Konferensartikel

Synthetic Skies Using High Dynamic Range Images and Eigenskies

B. A. Olsson
Linköping University, Sweden

A. Ynnerman
Linköping University, Sweden

R. Lenz
Linköping University, Sweden

Ladda ner artikelhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=010;article=008

Ingår i: The Annual SIGRAD Conference. Special Theme - Real-Time Simulations. Conference Proceedings from SIGRAD2003

Linköping Electronic Conference Proceedings 10:8, s. 35-39

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Publicerad: 2003-11-20

ISBN:

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

This paper presents a method to render synthetic sky images corresponding to given weather conditions. The method combines artificial neural networks and principal component analysis to associate the appearance of the sky with the state of a weather parameter vector. This association is then used to generate artificial sky images corresponding to a given weather parameter vector. The proposed method has important applications for example in flight simulators and in the game industry.

The skies are represented by high-dynamic-range images which are able to store the dynamic properties of sky light. This representation can be used for global illumination in software packages such as Radiance to render scenes at arbitrary lighting conditions. The results show that; although the cloud details can not be represented by this method it is possible to distinguish between different weather states.

CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color; shading and texture.

Nyckelord

Skylight; illumination and weather visualization

Referenser

CATS; G.; AND WOLTERS; L. 1997. The hirlam project. In IEEE Computational Science and Engineering; vol. 4; 22–31.

DAVINCI; L. 1970. The Notebooks of Leonardo Da Vinci. Dover. DEBEVEC; P. E.; AND MALIK; J. 1997. Recovering high dynamic range radiance maps from photographs. In Proceedings of SIGGRAPH 97; Computer Graphics Proceedings; 369–378.

DOBASHI; Y.; NISHITA; T.; KANEDA; T.; AND YAMASHITA; H. 1995. Fast display method of sky color using basis functions. In Pacific Graphics ’95.

HAASE; H.; BOCK; M.; HERGENROTHER; E.; KNOPFLE; C.; KOPPERT; H. J.; SCHRODER; F.; TREMBILSKI; A.; AND WEIDENHAUSEN; J. 2000. Meteorology meets computer graphics - look at a wide range of weather visualizations for diverse audiances. Computer & Graphics 24; 391–397.

HAGAN; M. T.; DEMUTH; H. B.; AND BEALE; M. 1995. Neural Network Design. PWS Publishing Company.

HAYKIN; S. 1999. Neural Networks. Prentice Hall.

HIBBARD; A.; AND SANTEK; D. 1990. The vis-5d system for easy interactive visualization. vol. 462; 28–35.

JOLIFFE; I. T. 1980. Principal Component Analysis. Springer.

MCGUIRE; P.; AND D’ELEUTERIO; G. M. T. 2001. Eigenpaxels and a neural-network approach to image classification. IEEE Transactions on neural networks 12; 625–635.

NIMEROFF; J. DORSEY; J.; AND RUSHMEIER; H. 1996. Implementation and analysis of an image-based global illumination framework for animated environments. IEEE Transactions on Visualization and Computer Graphics 2; 4.

OLSSON; B.; YNNERMAN; A.; AND LENZ; R. 2003. Skyvis; an application of matlab in meteorological visualization. In Proceedings of Nordic

MATLAB Conference 2003; 295–300.

PREETHAM; A.; SHIRLEY; P.; AND SMITS; B. 1999. A practical analytic model for daylight. In Proc. SIGGRAPH.

RILEY; K.; EBERT; D.; HANSEN; C.; AND LEVIT; J. 2003. Visually accurate multi-field weather visualization. In Proceedings of IEEE Visualization 2003.

RUMELHART; D. E.; HINTON; G. E.; AND WILLIAMS; R. J. 1986. Learning representations by back-propagating errors. Nature 323; 533–536.

SCALES; L. E. 1985. Introduction to Non-Linear Optimization. Springer- Verlag.

TURK; A.; AND PENTLAND; A. P. 1991. Face recognition using eigenfaces. J. Cognitive Neurosci. 3; 71–86.

WARD; G. J. 1994. The radiance lighting simulation and rendering system. In Proceedings of SIGGRAPH’94; ACM Press / ACM SIGGRAPH; ACM; 459–472.

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