An Iterative Quantum Approach for Transformation Estimation from Point Sets

Natacha Kuete Meli; Florian Mannel; Jan Lellmann

An Iterative Quantum Approach for Transformation Estimation from Point Sets

Natacha Kuete Meli^*, Florian Mannel, Jan Lellmann

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Mathematische Methoden der Bildverarbeitung

Abstract

We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

Originalsprache	Englisch
Titel	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Erscheinungsdatum	2022
Seiten	529-537
Publikationsstatus	Veröffentlicht - 2022

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title = "An Iterative Quantum Approach for Transformation Estimation from Point Sets",

abstract = "We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio. ",

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year = "2022",

language = "English",

pages = "529--537",

booktitle = "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",

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AU - Kuete Meli, Natacha

AU - Mannel, Florian

AU - Lellmann, Jan

PY - 2022

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N2 - We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

AB - We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.

M3 - Conference contribution

SP - 529

EP - 537

BT - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ER -

An Iterative Quantum Approach for Transformation Estimation from Point Sets

Abstract

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