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… Click to show full 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.
               
Click one of the above tabs to view related content.