A Comparative Study of Surface Normal Estimation Methods for Ground-Truth Surface Normal Generation

Surface normal estimation is a fundamental step in many applications, including robotics, manufacturing inspection and quality control. Reliable surface normal information is essential for accurate surface reconstruction, defect detection, and geometric analysis. In this paper, different approaches for estimating surface normal vectors from point cloud data are investigated, with particular focus on their suitability for generating ground-truth surface normals. Such ground-truth measurements are required to evaluate and validate surface normal estimation techniques used in practical metrology and 3D sensing systems in accordance with established surface texture standards such as ISO 25178. The analysis focuses on methods based on neighbourhood clustering followed by a surface normal fitting procedure. In addition, the influence of point cloud noise on the accuracy and stability of the estimated normals is systematically studied. By comparing the performance of different estimation strategies under different noise conditions, their suitability for generating reliable reference surface normals is investigated. The results demonstrate how point cloud characteristics and noise levels affect the robustness of surface normal estimation and highlight the advantages and limitations of commonly used approaches. The proposed analysis provides practical guidance for selecting appropriate methods when generating ground-truth surface normals for the validation of optical and geometric measurement systems. To show that the studied approaches can be used for surface normal estimation independent of metrology equipment, they are applied to the point clouds acquired from laser scanners, confocal optical measurement equipment, coherence scanning interferometers, and fringe projection systems with a variety of test objects. The results show that the studied approaches can estimate the surface normals. Highly accurate surface normal estimation may ease surface texture studies. The findings contribute to improving the reliability of point cloud analysis and surface normal estimation in manufacturing metrology and related applications.