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Theoretical Accuracy Limit

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Guide to 3D Vision Computation

Abstract

We derive here a theoretical accuracy limit of the geometric estimation problem in the general mathematical framework described in Chaps. 14 and 15. It is given in the form of a bound, called the KCR (Kanatani-Cramer-Rao) lower bound, on the covariance matrix of the solution \(\varvec{\theta }\). The resulting form indicates that all iterative algebraic and geometric methods achieve this bound up to higher-order terms in \(\sigma \), meaning that these are all optimal with respect to covariance. As in Chaps. 14 and 15, we treat \(\varvec{\theta }\) and \(\varvec{\xi }_{\alpha }\) as nD vectors for generality.

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References

  1. K. Kanatani, Statistical Optimization for Geometric Computation: Theory and Practice, Elsevier, Amsterdam, The Netherlands (1996) (Reprinted by Dover, New York, U.S., 2005)

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Correspondence to Kenichi Kanatani .

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Kanatani, K., Sugaya, Y., Kanazawa, Y. (2016). Theoretical Accuracy Limit. In: Guide to 3D Vision Computation. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-48493-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-48493-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48492-1

  • Online ISBN: 978-3-319-48493-8

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