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Latest revision as of 18:31, 3 February 2021

Abstract

The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications. We propose a quantum-enabled lossy 3d point cloud compression pipeline based on the constructive solid geometry (CSG) model representation. Key parts of the pipeline are mapped to NP-complete problems for which an efficient Ising formulation suitable for the execution on a Quantum Annealer exists. We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks of the proposed compression pipeline. Additionally, we discuss the properties of the overall pipeline regarding result optimality and described Ising formulations.

Comment: 6 pages, 3 figure


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/glocomw.2018.8644358
https://arxiv.org/pdf/2003.13253,
https://arxiv.org/abs/2003.13253,
https://ui.adsabs.harvard.edu/abs/2020arXiv200313253F/abstract,
http://export.arxiv.org/pdf/2003.13253,
https://fr.arxiv.org/abs/2003.13253,
https://ru.arxiv.org/abs/2003.13253,
https://www.arxiv-vanity.com/papers/2003.13253,
https://au.arxiv.org/abs/2003.13253,
https://export.arxiv.org/abs/2003.13253,
https://academic.microsoft.com/#/detail/2920627991
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.1109/glocomw.2018.8644358
Licence: CC BY-NC-SA license

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