Disclosed is an end-to-end speckle projection three-dimensional measurement method based on deep learning. The method comprises: first using a projector for projection and a binocular camera for synchronously collecting a speckle pattern; performing stereo correction on the speckle pattern, inputting the pattern into a stereo matching network, and a feature extraction sub-network based on a shared weight processing the speckle pattern obtain a series of low-resolution three-dimensional feature tensors; inputting the feature tensors into a salient object detection sub-network for detecting foreground information in the speckle pattern to generate a full-resolution effective mask; generating a four-dimensional matching cost body by means of the feature tensors of two viewing angles according to a candidate disparity range, achieving cost aggregation by means of a series of three-dimensional convolutional layer filtering, and obtaining an initial disparity map by means of disparity regression; and obtaining a final disparity map by combining the effective mask and the initial disparity map to achieve single-frame robust absolute three-dimensional surface contouring. The present invention can achieve single-frame robust absolute three-dimensional surface contouring simply by projecting a speckle pattern.
END-TO-END SPECKLE PROJECTION THREE-DIMENSIONAL MEASUREMENT METHOD BASED ON DEEP LEARNING