为了解决航空发动机机匣性能试验中双目DIC变形测量偏差难以全场、精确量化的问题，提出了一种系统、全面的DIC测量数据与有限元仿真数据之间的映射方法。首先，采用FPFH特征和ICP算法精确配准两类点云数据，完成了数据坐标系的精确对齐；然后使用遗传算法优化的拟合神经网络调整有限元节点位置，从而消除两类数据间节点位置不一致的问题，完成仿真网格向DIC网格的高精度映射；最后，使用逐点最小二乘应变估计算法统一了有限元仿真和DIC测量的应变计算模式，得到与DIC属性一致的有限元比对数据，从而实现被测面全场变形的偏差估计。机匣刚度实验中肋板处的变形比对结果显示，网格节点的映射精度优于1×10-6mm，仿真变形和DIC变形的偏差云图与偏差曲线具有良好的一致性，且能够显示DIC测量偏差存在的具体位置，在未来航空发动机机匣及类匣体的研制和测试领域具有良好的应用前景。

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.