Thickness Measurement Method for Panel Grids Based on Point Cloud Segmentation and Clustering*

The thickness measurement of panel grids plays an important role in ensuring its quality and load-bearing capacity. With the widespread application of 3D laser scanning technology, point cloud-based methods have provided an effective solution for accurate thickness measurement. This paper proposes an automated measurement method based on point cloud segmentation and clustering to estimate the thickness of multiple grids on the panel. First, an adaptive convolution-based point cloud segmentation network is designed to identify grid points from the input point cloud of panel. This segmentation network features a position adaptive convolution module and a direction adaptive convolution module. The former dynamically encodes spatial positions to capture local geometric relationships, while the latter learns the direction of the normal vector to distinguish global geometric differences. Second, the extracted grid points are then clustered using the DBSCAN algorithm to identify individual grid cells. Finally, the thickness of each grid cell is computed by measuring the distance between corresponding inner and outer surface points based on neighborhood normal vectors. Comparative experimental results demonstrate the effectiveness of the proposed thickness measurement method for panel grids.