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学者姓名：方莉娜
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Abstract ：
The category of road marking is a crucial element in Mobile laser scanning systems' (MLSs) applications such as intelligent traffic systems, high-definition maps, location and navigation services. Due to the complexity of road scenes, considerable and various categories, occlusion and uneven intensities in MLS point clouds, finely road marking classification is considered as the challenging work. This paper proposes a graph attention network named GAT_SCNet to simultaneously group the road markings into 11 categories from MLS point clouds. Concretely, the proposed GAT_SCNet model constructs serial computable subgraphs and fulfills a multi-head attention mechanism to encode the geometric, topological, and spatial relationships between the node and neighbors to generate the distinguishable descriptor of road marking. To assess the effectiveness and general-ization of the GAT_SCNet model, we conduct extensive experiments on five test datasets of about 100 km in total captured by different MLS systems. Three accuracy evaluation metrics: average Precision, Recall, and F-1 of 11 categories on the test datasets exceed 91%, respectively. Accuracy evaluations and comparative studies show that our method has achieved a new state-of-the-art work on road marking classification, especially on similar linear road markings like stop lines, zebra crossings, and dotted lines.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Graph neural network Graph neural network MLS points clouds MLS points clouds Road marking classification Road marking classification
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| GB/T 7714 | Fang, Lina , Sun, Tongtong , Wang, Shuang et al. A graph attention network for road marking classification from mobile LiDAR point clouds [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 108 . |
| MLA | Fang, Lina et al. "A graph attention network for road marking classification from mobile LiDAR point clouds" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 108 (2022) . |
| APA | Fang, Lina , Sun, Tongtong , Wang, Shuang , Fan, Hongchao , Li, Jonathan . A graph attention network for road marking classification from mobile LiDAR point clouds . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 108 . |
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Nowadays, researchers have developed various deep neural networks for processing point clouds effectively. Due to the enormous parameters in deep learning-based models, a lot of manual efforts have to be invested into annotating sufficient training samples. To mitigate such manual efforts of annotating samples for a new scanning device, this letter focuses on proposing a new neural network to achieve domain adaptation in 3-D object classification. Specifically, to minimize the data discrepancy of intraclass objects in different domains, an Asymmetrical Siamese (AS) module is designed to align the intraclass features. To preserve the discriminative information for distinguishing interclass objects in different domains, a Conditional Adversarial (CA) module is leveraged to consider the classification information conveyed from the classifier. To verify the effectiveness of the proposed method on object classification in heterogeneous point clouds, evaluations are conducted on three point cloud datasets, which are collected in different scenarios by different laser scanning devices. Furthermore, the comparative experiments also demonstrate the superior performance of the proposed method on the classification accuracy.
Keyword ：
3-D object classification 3-D object classification asymmetrical Siamese (AS) network asymmetrical Siamese (AS) network Data mining Data mining domain adaptation domain adaptation feature alignment feature alignment Feature extraction Feature extraction Generators Generators Neural networks Neural networks Point cloud compression Point cloud compression point clouds point clouds Three-dimensional displays Three-dimensional displays Training Training
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| GB/T 7714 | Luo, Huan , Li, Lingkai , Fang, Lina et al. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Luo, Huan et al. "Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Luo, Huan , Li, Lingkai , Fang, Lina , Wang, Hanyun , Wang, Cheng , Guo, Wenzhong et al. Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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Currently, mobile laser scanning (MLS) systems can conveniently and rapidly measure the backscattered laser beam properties of the object surfaces in large-scale roadway scenes. Such properties is digitalized as the in-tensity value stored in the acquired point cloud data, and the intensity as an important information source has been widely used in a variety of applications, including road marking inventory, manhole cover detection, and pavement inspection. However, the collected intensity is often deviated from the object reflectance due to two main factors, i.e. different scanning distances and worn-out surfaces. Therefore, in this paper, we present a new intensity-enhanced method to gradually and efficiently achieve the intensity enhancement in the MLS point clouds. Concretely, to eliminate the intensity inconsistency caused by different scanning distances, the direct relationship between scanning distance and intensity value is modeled to correct the inconsistent intensity. To handle the low contrast between 3D points with different intensities, we proposed to introduce and adapt the dark channel prior for adaptively transforming the intensity information in point cloud scenes. To remove the isolated intensity noises, multiple filters are integrated to achieve the denoising in the regions with different point densities. The evaluations of our proposed method are conducted on four MLS datasets, which are acquired at different road scenarios with different MLS systems. Extensive experiments and discussions demonstrate that the proposed method can exhibit the remarkable performance on enhancing the intensities in MLS point clouds.
Keyword ：
Dark Channel Prior Dark Channel Prior Intensity Enhancement Intensity Enhancement Mobile Laser Scanning Mobile Laser Scanning Point Cloud Point Cloud Point Cloud Denoising Point Cloud Denoising
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| GB/T 7714 | Fang, Lina , Chen, Hao , Luo, Huan et al. An intensity-enhanced method for handling mobile laser scanning point clouds [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 107 . |
| MLA | Fang, Lina et al. "An intensity-enhanced method for handling mobile laser scanning point clouds" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 107 (2022) . |
| APA | Fang, Lina , Chen, Hao , Luo, Huan , Guo, Yingya , Li, Jonathon . An intensity-enhanced method for handling mobile laser scanning point clouds . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2022 , 107 . |
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Urban management and survey departments have begun investigating the feasibility of acquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/ PGVNet.
Keyword ：
Attention mechanism Attention mechanism Deep learning Deep learning Mobile laser scanning systems Mobile laser scanning systems Multiview images Multiview images Point cloud classification Point cloud classification
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| GB/T 7714 | Fang, Lina , You, Zhilong , Shen, Guixi et al. A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2022 , 193 : 115-136 . |
| MLA | Fang, Lina et al. "A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 193 (2022) : 115-136 . |
| APA | Fang, Lina , You, Zhilong , Shen, Guixi , Chen, Yiping , Li, Jianrong . A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2022 , 193 , 115-136 . |
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交通标线是重要的交通安全设施,其位置、属性和拓扑关系精细刻画道路交通结构,是智能交通、高精地图、位置与导航等应用的基础数据.本文提出一种融合空间上下文信息的车载激光点云标线分类图注意力模型(graph attention network with spatial context information,GAT_SCNet).该模型利用图结构建立标线及其邻接对象的出现和依存关系,基于标线几何、拓扑、空间结构关系构建注意力机制进行节点特征动态更新,通过对节点分类实现标线的精细分类.基于分类后标线,设计不同方案实现对分类后标线提取标线矢量化数据.试验采用4份不同车载激光扫描系统获取的城市与高速场景数据验证本文方法的有效性,试验结果中9类标线分类的准确率分别为100.00％、93.77％、100.00％、100.00％、100.00％、96.73％、97.96％、100.00％、98.39％,召回率分别为100.00％、96.36％、100.00％、100.00％、100.00％、97.26％、85.72％、100.00％、94.16％.结果表明,本文方法能实现道路场景中全尺寸、多类型标线对象的精确识别,并对形状相似标线(如虚线、斑马线和停止线)的区分具有较强稳健性.
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| GB/T 7714 | 方莉娜 , 王爽 , 赵志远 et al. 车载激光点云中交通标线自动分类与矢量化 [J]. | 测绘学报 , 2021 , 50 (9) : 1251-1265 . |
| MLA | 方莉娜 et al. "车载激光点云中交通标线自动分类与矢量化" . | 测绘学报 50 . 9 (2021) : 1251-1265 . |
| APA | 方莉娜 , 王爽 , 赵志远 , 付化胜 , 陈崇成 . 车载激光点云中交通标线自动分类与矢量化 . | 测绘学报 , 2021 , 50 (9) , 1251-1265 . |
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Accurately identifying roadside objects like trees, cars, and traffic poles from mobile LiDAR point clouds is of great significance for some applications such as intelligent traffic systems, navigation and location services, autonomous driving, and high precision map. In the paper, we proposed a point-group-view network (PGVNet) to classify the roadside objects into trees, cars, traffic poles, and others, which utilize and fuse the advanced global features of multi-view images and the spatial geometry information of point cloud. To reduce redundant information between similar views and highlight salient view features, the PGVNet model employs a hierarchical view-group-shape architecture to split all views into different groups according to their discriminative level, which uses the pre-trained VGG network as the bone network. In view-group-shape architecture, global-level significant features are further generated from group descriptors with their weights. Moreover, an attention-guided fusion network is used to fuse the global features from multi-view images and local geometric features from point clouds. In particular, the global advanced features from multi-view images are quantified and leveraged as the attention mask to further refine the intrinsic correlation and discriminability of the local geometric features from point clouds, which contributions to recognize the roadside objects. We have evaluated the proposed method on five different mobile LiDAR point cloud data. Five test datasets of different urban scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected testing datasets achieve (99.19%, 94.27%, 93.58%, 96.63%), (94.20%, 97.56%, 92.02%, 95.68%), (91.48%, 98.61%, 90.39%, 94.87%), respectively. Experimental results and comparisons with state-of-the-art methods demonstrate that the PGVNet model is available to effectively identify roadside objects from the mobile LiDAR point cloud, which can provide data support for elements construction and vectorization in high precision map applications. © 2021, Surveying and Mapping Press. All right reserved.
Keyword ：
Classification (of information) Classification (of information) Deep learning Deep learning Forestry Forestry Geometry Geometry Laser applications Laser applications Optical radar Optical radar Poles Poles Quality control Quality control Roadsides Roadsides
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| GB/T 7714 | Fang, Lina , Shen, Guixi , You, Zhilong et al. A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds [J]. | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (11) : 1558-1573 . |
| MLA | Fang, Lina et al. "A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds" . | Acta Geodaetica et Cartographica Sinica 50 . 11 (2021) : 1558-1573 . |
| APA | Fang, Lina , Shen, Guixi , You, Zhilong , Guo, Yingya , Fu, Huasheng , Zhao, Zhiyuan et al. A joint network of point cloud and multiple views for roadside objects recognition from mobile laser point clouds . | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (11) , 1558-1573 . |
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Road markings are important traffic safety facilities. Its location, attribute, and topological relationship finely describe road traffic structure, and it is the basic data for applications such as intelligent traffic, high-precision maps, location, and navigation. This paper proposes a graph attention network with spatial context information (GAT_SCNet) to classify the road markings from mobile LiDAR point clouds. GAT_SCNet explores the graph structure to establish the appearance and dependence information among road markings. Meanwhile, GAT_SCNet incorporates the multi-head attention mechanism into the node propagation step, which computes the hidden states of each node based on the geometric, topological, and spatial structure relationships of the neighboring nodes. Finally, road markings classification is realized by the classification of nodes. Then, some schemes are designed for road markings vectorization. Four test datasets consisting of urban and highway scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision and recall of 9 types of road markings on the selected test datasets achieve (100.00%, 93.77%, 100.00%, 100.00%, 100.00%, 96.73%, 97.96%, 100.00%, 98.39%) and (100.00%, 96.36%, 100.00%, 10.000%, 100.00%, 97.26%, 85.72%, 100.00%, 94.16%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the capability of classifying multi-type road markings simultaneously and distinguishing similar road markings like dashed markings, zebra crossings, and stop lines in complex urban scenes. © 2021, Surveying and Mapping Press. All right reserved.
Keyword ：
Classification (of information) Classification (of information) Graphic methods Graphic methods Highway markings Highway markings Road and street markings Road and street markings Roads and streets Roads and streets Topology Topology
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| GB/T 7714 | Fang, Lina , Wang, Shuang , Zhao, Zhiyuan et al. Automatic classification and vectorization of road markings from mobile laser point clouds [J]. | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (9) : 1251-1265 . |
| MLA | Fang, Lina et al. "Automatic classification and vectorization of road markings from mobile laser point clouds" . | Acta Geodaetica et Cartographica Sinica 50 . 9 (2021) : 1251-1265 . |
| APA | Fang, Lina , Wang, Shuang , Zhao, Zhiyuan , Fu, Huasheng , Chen, Chongcheng . Automatic classification and vectorization of road markings from mobile laser point clouds . | Acta Geodaetica et Cartographica Sinica , 2021 , 50 (9) , 1251-1265 . |
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Due to the advantages of 3D point clouds over 2D optical images, the related researches on scene understanding in 3D point clouds have been increasingly attracting wide attention from academy and industry. However, many 3D scene understanding methods largely require abundant supervised information for training a data-driven model. The acquisition of such supervised information relies on manual annotations which are laborious and arduous. Therefore, to mitigate such manual efforts for annotating training samples, this paper studies a unified neural network to segment 3D objects out of point clouds interactively. Particularly, to improve the segmentation performance on the accurate object segmentation, the boundary information of 3D objects in point clouds are encoded as a boundary energy term in the Markov Random Field (MRF) model. Moreover, the MRF model with the boundary energy term is naturally integrated with the Graphical Neural Network (GNN) to obtain a compact representation for generating the boundary-preserved 3D objects. The proposed method is evaluated on two point clouds datasets obtained from different types of laser scanning systems, i.e. terrestrial laser scanning system and mobile laser scanning system. Comparative experiments show that the proposed method is superior and effective in 3D objects segmentation in different point-cloud scenarios.
Keyword ：
3D Object Segmentation 3D Object Segmentation Boundary Constraint Boundary Constraint Graph Neural Network Graph Neural Network Markov Random Field Markov Random Field Point Cloud Point Cloud
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| GB/T 7714 | Luo, Huan , Zheng, Quan , Fang, Lina et al. Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2021 , 104 . |
| MLA | Luo, Huan et al. "Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 104 (2021) . |
| APA | Luo, Huan , Zheng, Quan , Fang, Lina , Guo, Yingya , Guo, Wenzhong , Wang, Cheng et al. Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2021 , 104 . |
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Traffic facilities extraction is of vital importance to various applications such as intelligent transportation systems, infrastructures inventory and city management related applications. Mobile laser scanning (MLS) systems provide a new technique to capture and update traffic facilities information. However, classifying raw MLS point clouds into semantic objects is still one of the most challenging and important issues. In this study, we separate the raw off-ground point clouds into individual segments and explore an object-based Deep Belief Network (DBN) architecture to detect roadside traffic facilities (trees, cars, and traffic poles) with limited labeled samples. To deal with various roadside traffic objects with different types, sizes, orientations and levels of incompleteness, we develop a simple and general multi-view feature descriptor to characterize the global feature of individual objects and extend the quantity of the training samples. Extensive experiments are employed to evaluate the validities of the proposed algorithm with six test datasets acquired by different MLS Systems. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected MLS datasets achieve (96.08%, 97.61%, 93.86%, 96.81%), (97.55%, 94.10%, 91.69%, 95.58%) and (94.39%, 97.71%, 92.37%, 95.99%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the ability of achieving the promising performance of roadside traffic facilities detection in complex urban scenes.
Keyword ：
Automobiles Automobiles Biological system modeling Biological system modeling deep belief network deep belief network Feature extraction Feature extraction Machine learning Machine learning Mobile laser scanning Mobile laser scanning normalized cut normalized cut Semantics Semantics semantic segmentation semantic segmentation Solid modeling Solid modeling Three-dimensional displays Three-dimensional displays traffic facilities extraction traffic facilities extraction
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| GB/T 7714 | Fang, Lina , Shen, Guixi , Luo, Haifeng et al. Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (4) : 1964-1980 . |
| MLA | Fang, Lina et al. "Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22 . 4 (2021) : 1964-1980 . |
| APA | Fang, Lina , Shen, Guixi , Luo, Haifeng , Chen, Chongcheng , Zhao, Zhiyuan . Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (4) , 1964-1980 . |
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Semantic segmentation is a fundamental task in understanding urban mobile laser scanning (MLS) point clouds. Recently, deep learning-based methods have become prominent for semantic segmentation of MLS point clouds, and many recent works have achieved state-of-the-art performance on open benchmarks. However, due to differences of objects across different scenes such as different height of buildings and different forms of the same road-side objects, the existing open benchmarks (namely source scenes) are often significantly different from the actual application datasets (namely target scenes). This results in underperformance of semantic segmentation networks trained using source scenes when applied to target scenes. In this paper, we propose a novel method to perform unsupervised scene adaptation for semantic segmentation of urban MLS point clouds. Firstly, we show the scene transfer phenomena in urban MLS point clouds. Then, we propose a new pointwise attentive transformation module (PW-ATM) to adaptively perform the data alignment. Next, a maximum classifier discrepancy-based (MCD-based) adversarial learning framework is adopted to further achieve feature alignment. Finally, an end-to-end alignment deep network architecture is designed for the unsupervised scene adaptation semantic segmentation of urban MLS point clouds. To experimentally evaluate the performance of our proposed approach, two large-scale labeled source scenes and two different target scenes were used for the training. Moreover, four actual application scenes are used for the testing. The experimental results indicated that our approach can effectively achieve scene adaptation for semantic segmentation of urban MLS point clouds.
Keyword ：
Deep learning Deep learning Mobile laser scanning point clouds Mobile laser scanning point clouds Semantic segmentation Semantic segmentation Transfer learning Transfer learning Unsupervised scene adaptation Unsupervised scene adaptation
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| GB/T 7714 | Luo, Haifeng , Khoshelham, Kourosh , Fang, Lina et al. Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2020 , 169 : 253-267 . |
| MLA | Luo, Haifeng et al. "Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 169 (2020) : 253-267 . |
| APA | Luo, Haifeng , Khoshelham, Kourosh , Fang, Lina , Chen, Chongcheng . Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2020 , 169 , 253-267 . |
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