Laser Journal, Volume. 45, Issue 12, 174(2024)

Semantic segmentation of lightweight LiDAR point cloud images based on MobileNetV3 network

HAO Xia'nan1...2, PANG Yajun1,2,*, MA Yuexin1,2, and ZHANG Jiwen12 |Show fewer author(s)
Author Affiliations
  • 1Center for Advanced Laser Technology, Hebei University of Technology, Tianjin 300401, China
  • 2Hebei Key Laboratory of Advanced Laser Technology and Equipment, Tianjin 300401, China
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    To meet the challenge that the current semantic segmentation methods of LIDAR point cloud image are difficult to balance the processing speed and accuracy, and takes up huge computing resources, a lightweight algorithm based on codec structure is studied. This study firstly uses spherical projection to project 3D point clouds to two-dimensional planes, then designs a lightweight two-dimensional semantic segmentation network MobileSeg based on MobileNetV3 to segment projection map, and finally projects the segmentation results back to the 3D point cloud. The algorithm reduces the occupation of computing resources by reducing the dimensionality of point cloud images and lightweight backbone networks, and avoids various difficulties faced when using neural networks to directly extract features from sparse point clouds. The average intersectional union ratio (mIOU) of the algorithm on the SemanticKITTI dataset is 51.7%, and the parameters of the MobileSeg-S and MobileSeg-L models are 0.9M and 3.2 M, respectively. The accurate and lightweight point cloud semantic segmentation method has a certain application prospect in the field of automatic driving.

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    HAO Xia'nan, PANG Yajun, MA Yuexin, ZHANG Jiwen. Semantic segmentation of lightweight LiDAR point cloud images based on MobileNetV3 network[J]. Laser Journal, 2024, 45(12): 174

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    Paper Information

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    Received: Apr. 19, 2024

    Accepted: Mar. 10, 2025

    Published Online: Mar. 10, 2025

    The Author Email: Yajun PANG (yjpang@hebut.edu.cn)

    DOI:10.14016/j.cnki.jgzz.2024.12.174

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