SeqConv-Net: A Deep Learning Segmentation Framework for Airborne LiDAR Point Clouds Based on Spatially Ordered Sequences

SeqConv-Net: A Deep Learning Segmentation Framework for Airborne LiDAR Point Clouds Based on Spatially Ordered Sequences

Point cloud data provide three-dimensional (3D) information about objects in the real world, containing rich semantic features. Therefore, the task of semantic segmentation of point clouds has been widely applied in fields such as robotics and autonomous driving. Although existing research has made...

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Bibliographic Details
Main Authors: Bin Guo, Chunjing Yao, Hongchao Ma, Jie Wang, Junhao Xu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Remote Sensing
Subjects:
point cloud
semantic segmentation
recurrent neural network
convolutional neural network
seq2seq
Online Access:https://www.mdpi.com/2072-4292/17/11/1927
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