面向城市道路场景的车载LiDAR点云语义分割U形图卷积网络方法

doi:10.11947/j.AGCS.2025.20230481

测绘学报 ›› 2025, Vol. 54 ›› Issue (7): 1280-1293.doi: 10.11947/j.AGCS.2025.20230481

面向城市道路场景的车载LiDAR点云语义分割U形图卷积网络方法

万杰¹(), 谢忠²^,³(), 徐永洋², 陶留锋²^,³

^1.长江空间信息技术工程有限公司（武汉），湖北　武汉　430074
^2.中国地质大学（武汉）计算机学院，湖北　武汉　430074
^3.中国地质大学（武汉）地质探测与评估教育部重点实验室，湖北　武汉　430074

收稿日期:2023-10-17 修回日期:2025-04-17 出版日期:2025-08-18 发布日期:2025-08-18
通讯作者: 谢忠 E-mail:wanjie@cug.edu.cn;xiezhong@cug.edu.cn
作者简介:万杰（1993—），男，博士生，主要研究方向为三维点云智能分析处理。E-mail：wanjie@cug.edu.cn
基金资助:
地质探测与评估教育部重点实验室主任基金(GLAB2024ZR08);中央高校基本科研业务费

A U-shaped graph convolution network method for semantic segmentation of vehicle LiDAR point clouds towards urban road scenes

Jie WAN¹(), Zhong XIE²^,³(), Yongyang XU², Liufeng TAO²^,³

^1.Changjiang Spatial Information Technology Engineering Co., Ltd., (Wuhan), Wuhan 430074, China
^2.Department of Computer Science, China University of Geosciences, Wuhan 430074, China
^3.Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China

Received:2023-10-17 Revised:2025-04-17 Online:2025-08-18 Published:2025-08-18
Contact: Zhong XIE E-mail:wanjie@cug.edu.cn;xiezhong@cug.edu.cn
About author:WAN Jie (1993—), male, PhD candidate, majors in intelligent analysis and processing of 3D point clouds. E-mail: wanjie@cug.edu.cn
Supported by:
The Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(GLAB2024ZR08);The Fundamental Research Funds for the Central Universities

摘要/Abstract

摘要：

车载LiDAR点云语义分割旨在提取道路及其路侧多类地物目标的三维信息，对城市道路场景的目标对象化与三维建模至关重要。针对当前深度学习网络在处理车载LiDAR点云时，由于架构限制以及难以有效提取和利用多尺度信息而导致小尺寸目标、数据缺失和被遮挡目标分割不准确等问题，本文提出了一种基于U形图卷积网络(U-GCN)的点云语义分割方法。首先，设计了一个动态图卷积算子，利用可学习的点核自适应地提取点云局部几何特征，并通过级联的动态图卷积算子来构建局部特征聚合模块和扩大感受野，以捕获目标结构和上下文信息。然后，结合U形编码器-解码器网络架构，通过跳跃连接的方式融合深层和浅层点特征来获取多尺度细节信息，以增强目标特征表达。最后，引入深度监督损失函数，引导网络利用各层输出的预测信息进行多尺度的监督训练，进一步提升网络的稳健性和整体性能。在Toronto-3D和WHU-MLS数据集上试验表明，本文方法在可视化分析和定量评价方面均优于当前主流网络，能够有效改善因目标尺度变化、遮挡、数据缺失造成的分割精度低的问题。

关键词: 车载LiDAR点云, 语义分割, U形图卷积网络, 多尺度特征融合, 深度监督

Abstract:

Semantic segmentation of vehicle LiDAR point clouds aims to extract the 3D information of roads and various roadside objects, which is crucial for the objectification and 3D modeling of urban road scenes. Aiming at the challenges faced by current deep learning networks in handling vehicle LiDAR point clouds, including architectural constraints and difficulties in effectively extracting and utilizing multi-scale information, leading to inaccuracies in segmenting small objects, incomplete objects and occluded objects, this paper proposes a point cloud semantic segmentation method based on the U-shaped graph convolutional network (U-GCN). The proposed method firstly designed a dynamic graph convolutional operators that utilized learnable graph convolutional point kernels to adaptively extract local geometric features from the point cloud. Additionally, the cascaded dynamic graph convolutional operators were employed to construct a local feature aggregation module and expand the receptive field, enabling the capture of structural and contextual information on the objects. Subsequently, combined with the U-shaped encoder-decoder network architecture, deep and shallow point features are fused through skip connections to obtain multi-scale detailed information of objects, so as to enhance the feature representation of objects. Finally, a deep supervision loss function was introduced to guide the network to utilize output prediction information from different layers for the multiscale supervision training, further improving the network robustness and overall performance. Experiments on the Toronto-3D and WHU-MLS datasets show that the proposed method outperformed current mainstream networks in both visual analysis and quantitative evaluation. It can effectively improve the low segmentation accuracy caused by object scale variations, occlusion, and data incompleteness.

Key words: vehicle LiDAR point cloud, semantic segmentation, U-shaped graph convolution network, multiscale feature fusion, deep supervision

中图分类号:

P237

万杰, 谢忠, 徐永洋, 陶留锋. 面向城市道路场景的车载LiDAR点云语义分割U形图卷积网络方法[J]. 测绘学报, 2025, 54(7): 1280-1293.

Jie WAN, Zhong XIE, Yongyang XU, Liufeng TAO. A U-shaped graph convolution network method for semantic segmentation of vehicle LiDAR point clouds towards urban road scenes[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1280-1293.

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参考文献 36

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方法	OA	mAcc	mIoU	单个类别IoU
方法	OA	mAcc	mIoU	道路	路面标识	植被	建筑物	公用线路	电线杆	汽车	围栏
PointNet++（MSG）	93.3	74.3	61.6	93.5	37.3	89.6	82.4	45.1	61.1	73.1	10.3
DGCNN	95.3	78.2	69.3	95.2	33.5	95.2	91.8	78.9	72.0	72.0	16.5
RandLA-Net	95.4	76.3	71.4	94.8	0.0	95.3	92.6	86.4	71.4	90.7	40.3
BAF-LAC	95.4	—	70.2	94.8	0.0	95.8	92.3	80.2	73.8	90.5	33.8
NeiEA-Net	95.3	—	68.5	94.7	0.0	95.2	89.1	79.6	76.5	93.3	19.6
本文方法	96.4	82.9	79.0	95.6	33.8	97.2	93.0	86.7	81.0	93.4	51.5

方法	OA	mAcc	mIoU	单个类别IoU
方法	OA	mAcc	mIoU	植被	建筑物	车辆	行人	路灯	围栏	其他
PointNet++（MSG）	85.9	57.9	44.7	80.8	73.6	82.2	30.4	0.0	45.5	0.0
DGCNN	80.6	62.5	39.9	85.8	67.8	68.5	31.0	0.1	18.8	0.0
RandLA-Net	83.7	57.9	51.7	71.0	68.8	90.1	7.4	50.8	74.0	0.0
BAF-LAC	90.7	—	56.5	85.3	88.7	69.9	15.6	47.0	67.0	0.2
NeiEA-Net	88.8	—	54.8	80.3	85.1	88.7	22.4	36.7	70.5	0.0
本文方法	91.3	67.8	61.2	84.4	85.0	88.4	56.4	37.9	76.3	0.0

消融网络	动态图卷积算子	局部特征聚合模块	跳跃连接	深度监督损失	mIoU/（%）
A₁					36.9
A₂	√				48.7
A₃	√	√			52.4
A₄	√	√	√		58.0
A₅	√	√	√	√	61.2

方法	FLOPs/GB	参数/MB	推理时间/s	mIoU/（%）
PointNet++（MSG）	8 678.88	1.16	296.8	61.6
DGCNN	666.7	1.84	288.5	69.3
RandLA-Net	26.4	4.99	216.8	71.4
BAF-LAC	66.2	11.64	366.9	70.2
NeiEA-Net	34.0	4.87	247.7	68.5
本文方法	47.6	6.83	273.1	79.0

面向城市道路场景的车载LiDAR点云语义分割U形图卷积网络方法

A U-shaped graph convolution network method for semantic segmentation of vehicle LiDAR point clouds towards urban road scenes

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