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• W4206549407 startingPage "446" @default.
• W4206549407 abstract "Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms." @default.
• W4206549407 created "2022-01-26" @default.
• W4206549407 creator A5018533453 @default.
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• W4206549407 date "2022-01-18" @default.
• W4206549407 modified "2023-10-14" @default.
• W4206549407 title "Evaluating the Quality of Semantic Segmented 3D Point Clouds" @default.
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• W4206549407 doi "https://doi.org/10.3390/rs14030446" @default.
• W4206549407 hasPublicationYear "2022" @default.
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