3D reconstruction technology is one of the most popular research directions in machine vision, and has been widely used in the fields of unmanned driving and digital processing and production. Traditional 3D reconstruction methods include depth cameras and multi-line laser scanners, but the point clouds obtained by depth cameras have incomplete and inaccurate information, and the high cost of multi-line laser scanners hinders their application and research. To solve these problems, a three-dimensional reconstruction method based on a rotating two-dimensional laser scanner was proposed. First, a stepper motor was used to rotate a 2D laser scanner to obtain 3D point cloud data. Then, the position of the laser scanner was calibrated by multi-sensor fusion, and the point cloud data was matched by transforming the coordinate system. Finally, the collected point cloud data were filtered and simplified. The experimental results show that compared with depth camera/IMU data fusion, the reconstruction method’s average error of the proposed method is reduced by 0.93 mm, and it is 4.24 mm, the accuracy has reached the millimeter level, and the error rate is also controlled within 2%. The cost of the whole set of equipment is also greatly reduced compared to the multi-line laser scanner. It basically meets the requirements of high precision and low cost and retaining the shape characteristics of the object.