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High-density three-dimensional measurements through multilayer perceptron calibration and statistical band-limited patterns

机译:通过多层感知器校准和统计带限图形进行高密度三维测量

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摘要

Calibration of optical metrology stereophotog-rammetric systems is vital to obtain accurate and precise three-dimensional (3-D) measurements. Despite its importance, the work pipeline of intrinsic and extrinsic camera calibration still remains manually laborious with high technical complexity. The use of a multilayer perceptron neural network to calibrate an optical metrology stereophotog-rammetric system utilizing a statistical band-limited pattern projection system is demonstrated. Highly accurate, highly precise, and highly dense 3-D surface reconstructions are obtained solely from homologous corresponding pairs without the need for intrinsic and extrinsic camera calibration. Measurement performance in the typical optical metrology sense, where 3-D measurements were evaluated with respect to length and surface gauges, is shown.
机译:光学计量立体测光系统的校准对于获得准确和精确的三维(3-D)测量至关重要。尽管其重要性,内在和外在相机校准的工作流程仍然需要人工,且技术复杂度很高。演示了使用多层感知器神经网络校准利用统计带限图案投影系统的光学计量立体照相-测光系统。仅从同源的对应对中即可获得高精度,高精度和高密度3D表面重建,而无需进行内部和外部相机校准。显示了典型光学计量意义上的测量性能,其中针对长度和表面规格对3-D测量进行了评估。

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