A coarse aggregate gradation detection method based on 3D point cloud

Yang, Ming–Hsuan; Ding, Jiangang; Li, Wei; Tian, Aojia; Pei, Lili; Hao, Xin

doi:10.1016/j.conbuildmat.2023.131111

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Cited by 5 publications

References 32 publications

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A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

Shan,

Li,

Yang

et al. 2025

Expert Systems with Applications

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A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

Shan,

Li,

Yang

et al. 2025

Expert Systems with Applications

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Hyperbolic regularization-PointNet++: Automated three-dimensional soil particle roundness classification

Xi,

Zheng,

Gao

et al. 2024

Powder Technology

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A coarse aggregate particle size classification method by fusing 3D multi‐view and graph convolutional networks

Tian,

Li,

Yang

et al. 2024

Computer aided Civil Eng

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To address the inaccurate classification of coarse aggregate particle size due to insufficient height information in single‐view, a multi‐view and graph convolutional network (GCN) based method for coarse aggregate particle size classification was proposed in this study. First, the viewpoint selection and projection strategies were designed to build the aggregate multi‐view datasets. Then, the surface texture of the aggregate was reconstructed by using 3D point cloud information. Finally, self‐attention mechanism and three‐layer GCN were introduced to aggregate global shape feature descriptors. The experimental results show that the proposed interleaved self‐attention and view GCN model achieves a coarse aggregate particle size classification accuracy of 94.11%, outperforming other multi‐view classification algorithms. This method provides a new possibility for the accurate detection of aggregate particle size and provides significant support for the production and automatic detection of aggregate raw materials.

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A coarse aggregate gradation detection method based on 3D point cloud

Cited by 5 publications

References 32 publications

A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

A novel pipeline based on line-structured features and cross-camera perspectives for coarse aggregate point cloud registration

Hyperbolic regularization-PointNet++: Automated three-dimensional soil particle roundness classification

A coarse aggregate particle size classification method by fusing 3D multi‐view and graph convolutional networks

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