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L ∞ minimization in geometric reconstruction problems

Hartley, Richard; Schaffalitzky, Frederik

Description

We investigate the use of the L∞ cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sum-of-squares, or L 2 cost function that is commonly used (in least-squares fitting).

CollectionsANU Research Publications
Date published: 2004
Type: Conference paper
URI: http://hdl.handle.net/1885/79556
Source: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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