Improving the Performance of 3D Shape Measurement of Moving Objects by Fringe Projection and Data Fusion

Fringe projection profilometry (FPP) has been widely studied for measurements of three-dimensional (3D) shapes. However, the measurement moving objects is still a challenging problem associated with commonly used methods. Phase-shifting (PSP) suffers from motion-induced errors when applied to objects, while Fourier transform (FTP) sensitive background light and noise. This paper proposes novel data fusion approach improve by alleviating noise-induced errors. Firstly, set coarse on shape object are obtained at different time instants FTP. Then parameters motion over these retrieved estimates applying iterative closest point (ICP) method. Finally, utilized adaptively fuse based their quality. Simulations experiments verify that proposed can significantly accuracy.