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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 61; s. 1 - 14
Hlavní autoři:	Zeng, Tao, Luo, Fulin, Guo, Tan, Gong, Xiuwen, Xue, Jingyun, Li, Hanshan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!