The plenoptic illumination function

Image-based modeling and rendering has been demonstrated as a cost-effective and efficient approach to virtual reality applications. The computational model that most image-based techniques are based on is the plenoptic function. Since the original formulation of the plenoptic function does not incl...

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Bibliographic Details
Published in:IEEE transactions on multimedia Vol. 4; no. 3; pp. 361 - 371
Main Authors: Wong, Tien-Tsin, Fu, Chi-Wing, Heng, Pheng-Arm, Leung, Chi-Sing
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aggregates
Compressing
Computational modeling
Computer science
Cyclic redundancy check
Illumination
Image coding
Interpolation
Layout
Lighting
Mathematical analysis
Mathematical models
Parametrization
Rendering (computer graphics)
Representations
Virtual reality
ISSN:1520-9210, 1941-0077
Online Access:Get full text
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Summary:Image-based modeling and rendering has been demonstrated as a cost-effective and efficient approach to virtual reality applications. The computational model that most image-based techniques are based on is the plenoptic function. Since the original formulation of the plenoptic function does not include illumination, most previous image-based virtual reality applications simply assume that the illumination is fixed. We propose a formulation of the plenoptic function, called the plenoptic illumination function, which explicitly specifies the illumination component. Techniques based on this new formulation can be extended to support relighting as well as view interpolation. To relight images with various illumination configurations, we also propose a local illumination model, which utilizes the rules of image superposition. We demonstrate how this new formulation can be applied to extend two existing image-based representations, panorama representation such as QuickTime VR and two-plane parameterization, to support relighting with trivial modifications. The core of this framework is compression, and we therefore show how to exploit two types of data correlation, the intra-pixel and the inter-pixel correlations, in order to achieve a manageable storage size.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2002.802835