MBBOS-GCN: minimum bounding box over-segmentation—graph convolution 3D point cloud deep learning model

Zhan, Delong; Dong-dong, Liang; Jin, Huiyu; Wu, Xu

doi:10.1117/1.jrs.16.016502

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Therefore, this study opted to enhance point clouds with fewer points for certain tree species. In order to maintain the geometric structure and semantic information of the point cloud, we employ point cloud jittering to augment the point cloud data to meet the down-sampling requirements [51]. Specifically, we randomly selected some points from the point cloud data and added random numbers sampled from a normal distribution with a mean of 0 and a standard deviation of 0.01 to the three-dimensional coordinates of these points.…”

Section: Down-sampling Results Of Point Clouds After Enhancementmentioning

confidence: 99%

Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement

Fan

Wei

Zhang

et al. 2023

Forests

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Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of using the PointNet++ algorithm for point cloud tree species classification with airborne LiDAR data, we selected 11 tree species from the Minjiang River Estuary Wetland Park in Fuzhou City and Sanjiangkou Ecological Park. Training and testing sets were constructed through pre-processing and segmentation, and direct and enhanced down-sampling methods were used for tree species classification. Experiments were conducted to adjust the hyperparameters of the proposed algorithm. The optimal hyperparameter settings used the multi-scale sampling and grouping (MSG) method, down-sampling of the point cloud to 2048 points after enhancement, and a batch size of 16, which resulted in 91.82% classification accuracy. PointNet++ could be used for tree species classification using airborne LiDAR data with an insignificant impact on point cloud quality. Considering the differential distribution of the point cloud quantity, enhanced down-sampling yields improved the classification results compared to direct down-sampling. The MSG classification method outperformed the simplified sampling and grouping classification method, and the number of epochs and batch size did not impact the results.

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Section: Down-sampling Results Of Point Clouds After Enhancementmentioning

confidence: 99%

Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement

Fan

Wei

Zhang

et al. 2023

Forests

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“…PointNet [146], PointNet++ [147], PointSift [148], Engelmann [149], 3DContextNet [150], A-SCN [151], PointWeb [152], PAT [153], RandLA-Net [154], ShellNet [155], LSANeT [156] Point convolution PointCNN [157], DCNN [158], A-CNN [159], ConvPoint [160], KPCONV [161], DPC [162], InterpCNN [163] RNN-based RSNET [164], G+RCU [165], 3D-RNN [166] Graph-based DGCNN [167], SPG [168], SSP+SPG [169], GACNet [170], PAG [171], HDGCN [172], HPEIN [173], SPH3D-GCN [174], DPAM [175], MBBOS-GCN [186] Other methods Deep FusionNet [176], AMVNET [177],…”

Section: Strategy Methodsmentioning

confidence: 99%

Scanning Technologies to Building Information Modelling: A Review

et al. 2022

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Building information modelling (BIM) is evolving significantly in the architecture, engineering and construction industries. BIM involves various remote-sensing tools, procedures and standards that are useful for collating the semantic information required to produce 3D models. This is thanks to LiDAR technology, which has become one of the key elements in BIM, useful to capture a semantically rich geometric representation of 3D models in terms of 3D point clouds. This review paper explains the ‘Scan to BIM’ methodology in detail. The paper starts by summarising the 3D point clouds of LiDAR and photogrammetry. LiDAR systems based on different platforms, such as mobile, terrestrial, spaceborne and airborne, are outlined and compared. In addition, the importance of integrating multisource data is briefly discussed. Various methodologies involved in point-cloud processing such as sampling, registration and semantic segmentation are explained in detail. Furthermore, different open BIM standards are summarised and compared. Finally, current limitations and future directions are highlighted to provide useful solutions for efficient BIM models.

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“…Unmanned systems and augmented reality are examples of practical application scenarios where three-dimensional (3D) object recognition is more relevant than two-dimensional (2D) target recognition. Zhan et al [96] proposed a 3D point cloud model named minimum bounding box oversegmentation GCN (MBBOS-GCN). Tis model uses a minimum bounding box algorithm, and the farthest point sampling (FPS) algorithm is used to sample within each small region to reduce sampling randomness, with an accuracy of the model being more than 90% for segmentation of the 3D objects.…”

Section: Gcn For Computer Visionmentioning

confidence: 99%

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Bhatti

Tang

et al. 2023

International Journal of Intelligent Systems

112

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Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

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MBBOS-GCN: minimum bounding box over-segmentation—graph convolution 3D point cloud deep learning model

Cited by 6 publications

References 36 publications

Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement

Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement

Scanning Technologies to Building Information Modelling: A Review

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

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