Adaptive Grid Sampling Strategy for Free-form Surface Measurement Based on Reconstruction Bias Constraint

This paper proposes an adaptive grid sampling strategy based on reconstruction bias constraint for efficient and accurate measurement of free-form surfaces. The method first obtains initial grid points through uniform sampling, then calculates reconstruction errors through NURBS surface reconstruction, and adds sampling points to the areas with maximum errors to form a new sampling grid. This paper establishes a mathematical model including reconstruction bias and demonstrates through simulation experiments that this method reduces the number of sampling points by approximately 17.9%--24.0%compared to traditional uniform sampling, while significantly improving reconstruction accuracy. The method's applicability to free-form surfaces of varying complexity is also verified, providing a new approach to improving the efficiency of free-form surface measurement.