Three-Dimensional Reconstruction of Gearbox from Multi-View Point Clouds with Surface Feature Parameter Measurement Method

Accurate reconstruction of the three-dimensional(3D) information of a gearbox and precise acquisition of its surface features are crucial for ensuring coating quality. This paper proposes a method for 3D reconstruction of gearbox surface and measurement of characteristic parameters based on multi-view point clouds. A multi-view, multi-rotation visual acquisition system based on the kinect sensor was built to obtain comprehensive point cloud data of the workpiece model from six views. Then, the acquired point cloud data was preprocessed. Normal vector filtering(NF), Fruit fly optimization algorithm(FOA) modified point cloud erosion method and fast point feature histogram(FPFH) algorithm were applied for point cloud coarse registration, and generalized iterative closest point(GICP) method for accurate registration, thus completing point cloud 3D reconstruction. Subsequently, the contour, part height and curvature feature parameters of the reconstructed model were measured. Compared with ground-truth values, the method yields mean absolute errors of 6.47 mm for workpiece contour, 1.056 mm for part height, and 0.107 m⁻¹ for surface curvature, with corresponding relative accuracies of 98.7%, 93.125%, and 97.426%, respectively, where accuracy denotes the percentage of the computed mean relative to the actual mean. This study demonstrates the feasibility of the proposed approach for gearbox surface characterization and supports further 3D reconstruction and quantitative analysis. However, the current validation is limited to six gearboxes under controlled laboratory conditions; future work will address more challenging industrial scenarios involving oil contamination, occlusion, and variable reflections.