Surface Reconstruction of Defective Point Clouds Based on Dual Off-Set Gradient Functions

Kun, M.; Yin, Zhoupin

doi:10.5772/21087

Cited by 1 publication

(1 citation statement)

References 46 publications

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“…The NGS method calculates the normal vector of each point before sampling the point cloud, which can better preserve the details of 3D objects. The NGS method was first used to align multiple sets of point clouds representing the same object but with different coordinate systems [39], as well as for 3D point cloud surface reconstruction [40][41][42]. This method can effectively filter out the key points describing the shape of the object from a dense point cloud and is more conducive to the mutual matching of multiple groups of point clouds, as it can well-preserve the details of the 3D object's surface.…”

Section: Methods Combined With Non-uniform Grid and Farthest Point Sa...mentioning

confidence: 99%

Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

Liu

Huang

et al. 2022

Remote Sensing

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Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.

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Section: Methods Combined With Non-uniform Grid and Farthest Point Sa...mentioning

confidence: 99%

Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

Liu

Huang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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Surface Reconstruction of Defective Point Clouds Based on Dual Off-Set Gradient Functions

Cited by 1 publication

References 46 publications

Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

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