Recurrent Residual Dual Attention Network for Airborne Laser Scanning Point Cloud Semantic Segmentation

Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of airborne laser scanning (ALS) point clouds for comp...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 61; s. 1 - 14
Hlavní autori:	Zeng, Tao, Luo, Fulin, Guo, Tan, Gong, Xiuwen, Xue, Jingyun, Li, Hanshan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Aggregation Airborne lasers Attention mechanism Coders Convolution Decoding Design Distribution encoder-decoder structure Feature extraction Image segmentation Kernel kernel point convolution (KPConv) Kernels Laser applications Lasers Methods Modules Normal distribution Point cloud compression point cloud semantic segmentation recurrent residual structure Scaling Scaling factors Scanning Semantic segmentation Semantics Three-dimensional displays
ISSN:	0196-2892, 1558-0644
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