Fast High-Dimensional Filtering Using the Permutohedral Lattice

Many useful algorithms for processing images and geometry fall under the general framework of high‐dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non‐local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellat...

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Bibliographic Details
Published in:Computer graphics forum Vol. 29; no. 2; pp. 753 - 762
Main Authors: Adams, Andrew, Baek, Jongmin, Davis, Myers Abraham
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.05.2010
Subjects:
Algorithms
C (programming language)
Computer graphics
Filtering
Filters
Filtration
Gaussian
I.4.3 [Image Processing and Computer Vision]: Enhancement-Filtering
Image processing systems
Lattices
Noise levels
Systems development
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:Many useful algorithms for processing images and geometry fall under the general framework of high‐dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non‐local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high‐dimensional space with uniform simplices. Our algorithm is the first implementation of a high‐dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter‐free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive‐rate applications of filters in as high as eight dimensions.
Bibliography:ArticleID:CGF1645
istex:E64FC9E0C659CA7344DBE79C591832B5E9275E9A
ark:/67375/WNG-8VLMBVLB-L
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2009.01645.x