Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Abstract Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: 1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long distance measurement during point cloud measurement in dam areas. 2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. 3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. Experiments on a prototype arch dam show that the proposed normal estimation method improves the classification accuracy of PointNet + + from the original 96.26–98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy has improved by 0.82%, 1.22%, 0.22%, the mean intersection over union has improved by 0.0293, 0.0325, 0.0104. This study provides a high-precision classification scheme for applications such as dam deformation detection based on point cloud.