基于特征值的特征向量将点云特性描述成一个1×7的向量，分别从线性度、平

Based on the eigenvalue-based eigenvectors, the point cloud characteristics are described as a 1×7 vector, and the point cloud is described from multiple angles such as linearity, flatness, scattering, omnipotence, anisotropy, feature entropy, and curvature change. Let a, b, c denote the characteristic values ​​of the spatial tensor structure of the 3D point cloud, then extract a series of characteristic values ​​of the point cloud by the formula in Table 9.

Characteristic vectorbased on feature values describes the characteristics of point clouds as a vector of 1 x 7, describing point clouds from multiple angles such as linearity, plane, scattering, omnipotentness, anisotropy, feature entropy and curvature changes. Make a, b, c representthe the spatial space space structure characteristic value of a 3D point cloud, and a series of characteristic values of the point cloud are extracted from the formula in Table 9.

The feature vector based on eigenvalues describes the point cloud characteristics as a 1 × 7 vector, and describes the point cloud from the aspects of linearity, flatness, scattering, omnipotence, anisotropy, characteristic entropy and curvature change. Let a, B, C denote the spatial tensor structure eigenvalues of 3D point clouds, then a series of eigenvalues of point clouds are extracted by the formula in Table 9.<br>