An Iterative Quantum Approach for Transformation Estimation from Point Sets

Natacha Kuete Meli, Florian Mannel, Jan Lellmann

CVPR 2022 pp. 529-537

doi:10.1109/CVPR52688.2022.00061 /cvpr/2022/meli2022cvpr-iterative/

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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Text

Meli et al. "An Iterative Quantum Approach for Transformation Estimation from Point Sets." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.00061

Markdown

[Meli et al. "An Iterative Quantum Approach for Transformation Estimation from Point Sets." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/meli2022cvpr-iterative/) doi:10.1109/CVPR52688.2022.00061

BibTeX

@inproceedings{meli2022cvpr-iterative,
  title     = {{An Iterative Quantum Approach for Transformation Estimation from Point Sets}},
  author    = {Meli, Natacha Kuete and Mannel, Florian and Lellmann, Jan},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2022},
  pages     = {529-537},
  doi       = {10.1109/CVPR52688.2022.00061},
  url       = {https://mlanthology.org/cvpr/2022/meli2022cvpr-iterative/}
}