Aiming to address the problems of poor accuracy and low efficiency in modeling gear surfaces, as well as the lack of surface detail information in three-dimensional (3D) reconstruction models of laser point clouds, a point cloud registration and modeling method for gear surfaces based on laser and vision fusion is proposed. First, considering the high similarity between 3D features of the complete point cloud of the gear surface and the point cloud of the registration overlap area, a coarse registration method suitable for point clouds of gear surfaces is proposed. Second, aiming to address the issues of low convergence speed and poor accuracy of the iterative closest point (ICP) algorithm, improvements are made to the ICP algorithm by introducing curvature and voxel grid filtering to realize point cloud fine registration. Finally, to address the lack of surface detail information, such as color and texture, in 3D reconstruction models of laser point clouds, the checkerboard calibration method and the direct linear transform (DLT) method are combined to solve for the camera pose parameters . The laser point cloud is colored by the point cloud library (PCL) to realize the realistic modeling of gear surfaces. The experimental results indicate that compared with the traditional registration method, the proposed method reduces the root mean square error by 61% and the registration time by 53%. Unlike laser point cloud modeling, the proposed 3D reconstruction model incorporates surface texture information.