What is the best triangulation approach for a structured light system?

It has become customary to calibrate a camera-projector pair in a structured light (SL) system as a stereo-vision setup. The 3D reconstruction is carried out by triangulation from the detected point at the camera sensor and its correspondence at the projector DMD. There are several algebraic formulations to obtain the coordinates of the 3D point, especially in the presence of noise. However, it is not clear what is the best triangulation approach. In this study, we aimed to determine the most suitable triangulation method for SL systems in terms of accuracy and execution time. We assess different strategies in which both coordinates in the projector are known (point-point correspondence) and the case in which only the one coordinate in the DMD is known (pointline correspondence). We also introduce the idea of estimating the second projector coordinate with epipolar constraints. We carried out simulations and experiments to evaluate the differences between the triangulation methods, considering the phase-depth sensitivity of the system. Our results show that under suboptimal phasedepth sensitivity conditions, the triangulation method does influence the overall accuracy. Therefore, the system should be arranged for optimal phase-depth sensitivity so that any triangulation method ensures the same accuracy.

[1]  Lenny A. Romero,et al.  Hybrid calibration procedure for fringe projection profilometry based on stereo-vision and polynomial fitting , 2020, Applied optics.

[2]  Song Zhang,et al.  High-speed 3D imaging with digital fringe projection techniques , 2016, Optical Engineering + Applications.

[3]  R. Vargas,et al.  Camera-Projector Calibration Methods with Compensation of Geometric Distortions in Fringe Projection Profilometry: A Comparative Study , 2018, Optica Pura y Aplicada.

[4]  Zhan Song,et al.  Method for calibration accuracy improvement of projector-camera-based structured light system , 2017 .

[5]  Yajun Wang,et al.  Optimal fringe angle selection for digital fringe projection technique. , 2013, Applied optics.

[6]  M. Takeda,et al.  Fourier transform profilometry for the automatic measurement of 3-D object shapes. , 1983, Applied optics.

[7]  Liandong Yu,et al.  Sub-pixel projector calibration method for fringe projection profilometry. , 2017, Optics express.

[8]  Beiwen Li,et al.  Structured light system calibration method with optimal fringe angle. , 2014, Applied optics.

[9]  Peisen S. Huang,et al.  Novel method for structured light system calibration , 2006 .

[10]  Ken Chen,et al.  Depth-driven variable-frequency sinusoidal fringe pattern for accuracy improvement in fringe projection profilometry. , 2018, Optics express.

[11]  Huanhuan Li,et al.  Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry , 2019 .

[12]  Ping Zhou,et al.  Analysis of the relationship between fringe angle and three-dimensional profilometry system sensitivity. , 2014, Applied optics.

[13]  Raúl Vargas,et al.  Evaluating the Influence of Camera and Projector Lens Distortion in 3D Reconstruction Quality for Fringe Projection Profilometry , 2018 .

[14]  Hongwei Guo,et al.  Geometric analysis of influence of fringe directions on phase sensitivities in fringe projection profilometry. , 2016, Applied optics.