基于多目立体视觉和神经网络标定的表面形貌测量方法研究

Non-contact optical measurement of three-dimensional surface topography is an important application of the computer multi-view stereo vision technology. However, there are still some technical problems needed to be solved such as limited number of cameras, complex image matching algorithms and insufficient longitudinal measurement accuracy. The article develops an optical method for surface topography measurement based on multi-view stereo vision and neural network calibration. This algorithm includes: applying neural networks for the calibration of the multi-view cameras and the three-dimensional reconstruction of feature points, projecting a laser speckle pattern on the surface as the feature points for image recognition and matching, using the particle tracking velocity technology to match the same single feature point in the multi-view images. As proved by experiments, compared with the traditional stereo vision measurement system, which is calibrated based on the pinhole camera model and matched by the epipolar constraint or cross-correlation algorithm, the method proposed in this paper can be adapted to scenes with large optical distortion, besides, the spatial resolution of the surface topography measurement is effectively improved and the measurement error in the depth direction is at the level of 1.0% - 2.0%.