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Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds Cover

Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds

Foundations of Computing and Decision Sciences
Volume 50 (2025): Issue 3 (September 2025)
By: Konrad Cop,  Bartosz Sułek and  Tomasz Trzciński  
Open Access
|Aug 2025

Authors

Konrad Cop

cop.konrad@gmail.com

Warsaw University of Technology, Faculty of Electronics and Information Technology, Warsaw, Poland
United Robots Sp. z o.o., Warsaw, Poland

Bartosz Sułek

United Robots Sp. z o.o., Warsaw, Poland

Tomasz Trzciński

Warsaw University of Technology, Faculty of Electronics and Information Technology, Warsaw, Poland
DOI: https://doi.org/10.2478/fcds-2025-0013 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
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Language: English
Page range: 347 - 371
Submitted on: Dec 1, 2024
|
Accepted on: Jun 17, 2025
|
Published on: Aug 21, 2025
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
deep learning for visual perception,
semantic segmentation,
3D Li-DAR,
robotic perception,
point clouds
Related subjects:
Computer sciences,
Artificial intelligence,
Software development

© 2025 Konrad Cop, Bartosz Sułek, Tomasz Trzciński, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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