为了提高基于结构光法的3维重建精度，采用机器学习中的回归模型对物体进行了3维形貌测量，通过以单目式获取对象高度点不同方向的光强信息簇样本，将其作为回归模型的训练集，在训练好回归模型后，直接建立起条纹图案的光强信息分布与对象高度之间的映射函数关系，完成对目标的3维测量;将调制条纹光数值信息以特征形式导入回归模型，获得端到端高度信息，验证了机器学习的神经网络回归模型在3维面形重建上的可行性。结果表明，该模型即使在投影特征模糊或噪音较大的情况也能较精确地重建3维面形，平均重建误差为1.40×10-4 mm，优于一般面形重建方法的数据。该研究为物体在强干扰条件下的单目式高精度3维面形重建提供了参考，简化了繁琐的计算过程和测量过程，提高了测量精度。

For the purpose of enhancing the precision of 3-D reconstruction based on the structured light method, the regression model in machine learning was used to measure the 3-D topography of objects. The light intensity information cluster samples in different directions of object height points were obtained monocular as the training set of the regression model. After the regression model was trained, the mapping function relationship between the illumination intensity information distribution of the modulation diagram and the height of the object can be directly established to complete the three-dimensional measurement of the object. The numerical information of modulated fringe light was introduced into the regression model in the form of characteristics. 3-D surface of the object was accurately reconstructed, and the purpose of obtaining the height information from end to end was realized. The feasibility of the neural network regression model based on machine learning in 3-D surface reconstruction was verified. The results show that the model can reconstruct the 3-D surface accurately even when the projection features are fuzzy or the noise is large. The average reconstruction error is 1.40×10-4 mm, which is better than the data of the general reconstruction method. This study provides a reference for the high-precision 3-D surface reconstruction of monocular objects under strong interference conditions, effectively simplifies the tedious calculation and measurement process, and improves measurement accuracy.