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

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Published in:Foundations of Computing and Decision Sciences
Format: Online Article RSS Article
Published: 2025
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container_title Foundations of Computing and Decision Sciences
description
discipline_display Computer Science
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format Online Article
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genre Journal Article
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institution FRELIP
journal_source_facet Foundations of Computing and Decision Sciences
publishDate 2025
publishDateSort 2025
record_format rss_article
spellingShingle Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
Computer Science
General
Computer Science
sub_discipline_display General
sub_discipline_facet General
subject_display Computer Science
General
Computer Science
Computer Science
General
Computer Science
subject_facet Computer Science
General
Computer Science
title Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_auth Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_full Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_fullStr Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_full_unstemmed Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_short Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds
title_sort annotation-free generation of training data using mixed domains for segmentation of 3d lidar point clouds
topic Computer Science
General
Computer Science
url https://sciendo.com/article/10.2478/fcds-2025-0013