The guide wheel is an important part of the hydraulic torque converter, and its 3D model plays an important role in the study of the performance parameters of the guide wheel. In order to solve the problem of detecting and reconstructing the complex guide wheel blade, the 3D point cloud data of the guide wheel is obtained by using the line laser scanner, the point cloud is processed by noise reduction using K-D Tree combined with bilateral filtering algorithm, the point cloud is refined by introducing the Mean Shifting clustering algorithm based on Gaussian sphere on the basis of k-means clustering algorithm, the refined data is reconstructed by surface reconstruction and The proposed method is used in conjunction with CATIA software to reconstruct the 3D model of the hydraulic torque converter guide wheel. The results show that the proposed method has good filtering and noise reduction effects, and the streamlined point cloud not only preserves the geometrical features of the guide wheel, but also improves the computational efficiency of the reconstruction algorithm. Analysis of the alignment with the point cloud obtained from the contact measurement with an error of ±0.1mm, with the number of point clouds greater than the threshold deviation not exceeding %5 of the overall point cloud.which can obtain a three-dimensional reconstruction model that meets the accuracy requirements and provides a basis for the three-dimensional smooth numerical simulation of the guide wheel and the optimization of performance parameters.