A method to systematically map DIC measurement data to finite element simulation data is proposed. This addresses challenges in quantifying binocular DIC deformation measurement discrepancies in full-field during the performance testing of aircraft engine casing. Initially， two sets of point cloud data were accurately registered using the FPFH feature and ICP algorithm， achieving precise alignment of their coordinate systems. Subsequently， a fitting neural network optimized by a genetic algorithm was employed to adjust the positions of finite element nodes. This ensured consistency in node positions between both data types， facilitating high-precision mapping from the simulation grid to the DIC grid. By implementing a point-by-point least squares strain estimation algorithm， the strain calculation techniques of both the finite element and DIC methods were aligned. Hence， finite element data that matches DIC attributes was produced， enabling estimation of full-field deformation deviations on the measured surface. The deformation comparison， particularly on the rib plate during the casing stiffness experiment， revealed a mapping accuracy of the mesh nodes better than 1×10^-6 mm. Deviation images comparing simulated and DIC deformations aligned well with the deviation curve， clearly indicating the locations of DIC measurement discrepancies. This method holds significant promise for applications in the development and testing of aircraft engine casings and box-like structures.