Abstract
Calibration of optical metrology stereophotogrammetric 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 stereophotogrammetric 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.
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