In recent years, Deep Learning (DL) techniques and large amounts of pointwise labels are employed to segment point clouds of the built environment. However, annotating pointwise labels is a time-consuming task. To address this issue, we propose a label-efficient DL network that obtains per-point semantic labels of LoD3 (Level-of-Detail) building point clouds with limited supervision. Experimentally, we compared our approach to the fully supervised DL methods, and we find our approach achieved comparable results on the ArCH Data Set, with only 10% of labelled training data obtained from fully supervised methods as input.

Label-efficient Deep Learning-based Semantic Segmentation of Building Point Clouds at LoD3 Level

Y. Cao;M. Scaioni
2021

Abstract

In recent years, Deep Learning (DL) techniques and large amounts of pointwise labels are employed to segment point clouds of the built environment. However, annotating pointwise labels is a time-consuming task. To address this issue, we propose a label-efficient DL network that obtains per-point semantic labels of LoD3 (Level-of-Detail) building point clouds with limited supervision. Experimentally, we compared our approach to the fully supervised DL methods, and we find our approach achieved comparable results on the ArCH Data Set, with only 10% of labelled training data obtained from fully supervised methods as input.
AsitaAcademy2021: Geomatica: il passato futuro
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1205247
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