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Plenoptic sampling (2000)
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| Venue: | In SIGGRAPH |
| Citations: | 248 - 14 self |
BibTeX
@INPROCEEDINGS{Chai00plenopticsampling,
author = {Jin-xiang Chai and Xin Tong and Shing-chow Chan and Heung-yeung Shum},
title = {Plenoptic sampling},
booktitle = {In SIGGRAPH},
year = {2000},
pages = {307--318}
}
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Abstract
This paper studies the problem of plenoptic sampling in imagebased rendering (IBR). From a spectral analysis of light field signals and using the sampling theorem, we mathematically derive the analytical functions to determine the minimum sampling rate for light field rendering. The spectral support of a light field signal is bounded by the minimum and maximum depths only, no matter how complicated the spectral support might be because of depth variations in the scene. The minimum sampling rate for light field rendering is obtained by compacting the replicas of the spectral support of the sampled light field within the smallest interval. Given the minimum and maximum depths, a reconstruction filter with an optimal and constant depth can be designed to achieve anti-aliased light field rendering. Plenoptic sampling goes beyond the minimum number of images needed for anti-aliased light field rendering. More significantly, it utilizes the scene depth information to determine the minimum sampling curve in the joint image and geometry space. The minimum sampling curve quantitatively describes the relationship among three key elements in IBR systems: scene complexity (geometrical and textural information), the number of image samples, and the output resolution. Therefore, plenoptic sampling bridges the gap between image-based rendering and traditional geometrybased rendering. Experimental results demonstrate the effectiveness of our approach.
Keyphrases
plenoptic sampling spectral support minimum sampling rate maximum depth light field signal anti-aliased light field minimum sampling curve minimum number spectral analysis image-based rendering imagebased rendering sampling theorem ibr system light field geometry space key element analytical function output resolution textural information paper study scene complexity image sample sampled light field light field rendering joint image constant depth reconstruction filter experimental result depth variation traditional geometrybased rendering scene depth information
