Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-26T11:32:38.766Z Has data issue: false hasContentIssue false
  • English
  • Français

EXPLICIT ANNOTATED 3D-CNN DEEP LEARNING OF GEOMETRIC PRIMITIVES INSTANCES – CORRIGENDUM

Published online by Cambridge University Press:  25 March 2024

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
Type
Corrigendum
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3951
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

https://doi.org/10.1017/pds.2023.178, Published by Cambridge University Press, 19th June 2023

The authors would like to correct an author name in the above article. The correct name for the third author is: Kristin Paetzold-Byhain.

The authors apologise for the error.

References

Hilbig, A., Holtzhausen, S. and Paetzold-Byhain, K. (2023) “Explicit Annotated 3D-CNN Deep Learning of Geometric Primitives Instances,” Proceedings of the Design Society, 3, pp. 1775-1784. doi: 10.1017/pds.2023.178.CrossRefGoogle Scholar