针对传统的单幅散斑图像匹配算法测量精度低且无法测量复杂面型物体等问题，提出了一种基于深度学习的散斑投影轮廓术，即通过深度学习的方法实现散斑图像的逐像素匹配。设计利用孪生卷积神经网络结构，将目标散斑图像和参考散斑图像以图像块的形式输入神经网络。通过卷积层运算提取散斑图像块的特征信息，进而将子网络得到的特征信息融合为两个图像块之间的匹配系数，以获得散斑图像的视差数据，并最终可将视差数据转化为物体的三维信息。实验结果表明，该方法可以通过单幅散斑图像实现精度约为290 μm的三维轮廓测量。

Traditional single speckle pattern matching algorithms always suffer from the low measurement accuracy and cannot be used to measure complex surface objects. A speckle projection profilometry with deep learning was proposed to realize the pixel-by-pixel matching. The siamese convolutional neural network structure was applied and extended where the main speckle pattern and the auxiliary speckle pattern were fed into the neural network patch by patch. It was expected that the feature from the speckle pattern patches could be extracted by the convolution operation. In this way, the features were fused and the matching coefficient between the two patches was obtained, which could be further used to formulate the disparity data and then the three-dimensional (3D) object was reconstructed. The experiment results demonstrate that with the proposed method 3D measurement with an accuracy of about 290 μm could be achieved through a single speckle pattern.