Comparative analysis of laser and simulated speckle pattern for single shot 3D reconstruction

Stereo photogrammetry is a fundamental technique for 3D measurements in different applications. As passive stereo methods struggle in texture-less regions, different light-coded patterns are projected to solve the correspondence problem. When the reconstruction method requires a series of sequentially coded light patterns, the system is prone to movement-related errors. A single pattern is utilized where the potential subjects are dynamic or change the position rapidly. A random speckle pattern is a good candidate for such systems. Primarily, there are two approaches to generate the speckle pattern in stereoscopic systems. The speckles can be generated by the coherent illumination of a laser diode, and the laser speckle-like pattern can also be simulated and projected by a professional projector. The purpose of both is to solve the stereo correspondence problem; however, the performance of both can differ, subjective to employed 3D sensing algorithm. In this Letter, we compare the performance of both for single-shot 3D reconstruction. We have discussed the pros and cons of both methods and analyze their ability to resolve long range 3D reconstruction.

[1]  Fuqiang Zhong,et al.  RGB laser speckles based 3D profilometry , 2019 .

[2]  Peter Kühmstedt,et al.  Experimental comparison of laser speckle projection and array projection for high-speed 3D measurements , 2015, Optical Metrology.

[3]  Jigui Zhu,et al.  Projector distortion residual compensation in fringe projection system , 2019 .

[4]  Haibo Lin,et al.  Speckle mechanism in holographic optical imaging. , 2007, Optics express.

[5]  Min Young Kim,et al.  High-density single shot 3D sensing using adaptable speckle projection system with varying preprocessing , 2021 .

[6]  Michael T. Orchard,et al.  Gradient-based residual variance modeling and its applications to motion-compensated video coding , 2001, IEEE Trans. Image Process..

[7]  Ling Shao,et al.  Enhanced Computer Vision With Microsoft Kinect Sensor: A Review , 2013, IEEE Transactions on Cybernetics.

[8]  T. Smausz,et al.  Homogenization with coherent light illuminated beam shaping diffusers for vision applications: spatial resolution limited by speckle pattern , 2018, Journal of the European Optical Society-Rapid Publications.

[9]  Martin Schaffer,et al.  High-speed pattern projection for three-dimensional shape measurement using laser speckles. , 2010, Applied optics.

[10]  Song Zhang,et al.  Ultrafast 3-D shape measurement with an off-the-shelf DLP projector. , 2010, Optics express.

[11]  Song Zhang,et al.  High-speed 3D shape measurement with structured light methods: A review , 2018, Optics and Lasers in Engineering.

[12]  Chang Liu,et al.  An improved spatiotemporal correlation method for high-accuracy random speckle 3D reconstruction , 2018, Optics and Lasers in Engineering.

[13]  P. Schwille,et al.  Flat-top TIRF illumination boosts DNA-PAINT imaging and quantification , 2019, Nature Communications.

[14]  Dong Liu,et al.  IR stereo RealSense: Decreasing minimum range of navigational assistance for visually impaired individuals , 2017, J. Ambient Intell. Smart Environ..

[15]  J. Cariou,et al.  Particle aggregation monitoring by speckle size measurement; application to blood platelets aggregation. , 2004, Optics express.

[16]  B. Dong,et al.  Fluorescent digital image correlation applied for macroscale deformation measurement , 2020 .

[17]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Reinhard Voelkel,et al.  Laser beam homogenizing: limitations and constraints , 2008, Optical Systems Design.

[19]  Alex Olwal,et al.  SpeckleSense: fast, precise, low-cost and compact motion sensing using laser speckle , 2011, UIST '11.

[20]  Min Young Kim,et al.  Single shot laser speckle based 3D acquisition system for medical applications , 2018, Optics and Lasers in Engineering.

[21]  Ruikang K. Wang,et al.  Statistics of local speckle contrast. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[22]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[23]  Peter F. Sturm,et al.  Triangulation , 1997, Comput. Vis. Image Underst..

[24]  G. Gu A comparative study of random speckle pattern simulation models in digital image correlation , 2015 .

[25]  Anand Asundi,et al.  Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry , 2010 .