Frequency Shift Triangulation: A Robust Fringe Projection Technique for 3D Shape Acquisition in the Presence of Strong Interreflections

We present the Frequency Shift Method, a new structured light technique allowing 3D shape acquisition in the presence of strong interreflections. The intensity signal of each camera pixel is represented by one or several peaks in the Fourier domain. If there is no interreflection, only a single peak appears, otherwise several peaks are present. Each peak represents a projector pixel participating in the illumination of the surface point imaged at the camera pixel. In the baseline version of the proposed approach, the number of patterns required is proportional to the number of projector pixels. We also propose a modification that significantly reduces the required number of patterns by subdividing and encoding the projector into multiple virtual low-resolution projectors. A method based on dynamic programming is used to separate direct and indirect illuminations. Our experimental results illustrate the effectiveness of our method compared to existing ones.

[1]  Daniel G. Aliaga,et al.  An Adaptive Correspondence Algorithm for Modeling Scenes with Strong Interreflections , 2009, IEEE Transactions on Visualization and Computer Graphics.

[2]  Ding Liu,et al.  Frequency-Based 3D Reconstruction of Transparent and Specular Objects , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Yee-Hong Yang,et al.  Frequency-based environment matting , 2004, 12th Pacific Conference on Computer Graphics and Applications, 2004. PG 2004. Proceedings..

[4]  Jason Geng,et al.  Structured-light 3D surface imaging: a tutorial , 2011 .

[5]  Sam Van der Jeught,et al.  Real-time structured light profilometry: a review , 2016 .

[6]  Clemens Rabe,et al.  Real-time Semi-Global Matching on the CPU , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[7]  Xianyu Su,et al.  Fourier transform profilometry:: a review , 2001 .

[8]  Gabriel Taubin,et al.  Embedded phase shifting: Robust phase shifting with embedded signals , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Shree K. Nayar,et al.  Multiplexed illumination for scene recovery in the presence of global illumination , 2011, 2011 International Conference on Computer Vision.

[10]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Stefan K. Gehrig,et al.  A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching , 2009, ICVS.

[12]  Hans-Peter Seidel,et al.  Polarization and Phase-Shifting for 3D Scanning of Translucent Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  H. Hirschmüller Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[14]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, ACM Trans. Graph..

[15]  Shree K. Nayar,et al.  Micro Phase Shifting , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Guy Godin,et al.  High Resolution Projector for 3D Imaging , 2014, 2014 2nd International Conference on 3D Vision.

[17]  Nicolas Martin,et al.  Unstructured Light Scanning Robust to Indirect Illumination and Depth Discontinuities , 2014, International Journal of Computer Vision.

[18]  Mohit Gupta,et al.  A Geometric Perspective on Structured Light Coding , 2018, ECCV.

[19]  Hans-Peter Seidel,et al.  Modulated phase-shifting for 3D scanning , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Yee-Hong Yang,et al.  Scene adaptive structured light using error detection and correction , 2015, Pattern Recognit..

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

[22]  Huijie Zhao,et al.  3D shape measurement in the presence of strong interreflections by epipolar imaging and regional fringe projection. , 2018, Optics express.

[23]  Matthew O'Toole,et al.  3D Shape and Indirect Appearance by Structured Light Transport , 2014, CVPR.

[24]  Joaquim Salvi,et al.  A state of the art in structured light patterns for surface profilometry , 2010, Pattern Recognit..

[25]  Avinash C. Kak,et al.  3D Modeling of Optically Challenging Objects , 2008, IEEE Transactions on Visualization and Computer Graphics.

[26]  Philippe Bekaert,et al.  Depth from sliding projections , 2009, CVPR.

[27]  Anand K. Asundi,et al.  Micro Fourier Transform Profilometry (μFTP): 3D shape measurement at 10, 000 frames per second , 2017, ArXiv.

[28]  J.-Angelo Beraldin,et al.  Characterizing the impact of optically induced blurring of a high-resolution phase-shift 3D scanner , 2017, Machine Vision and Applications.

[29]  Alan Boate,et al.  Structured light 3D measurement of reflective objects using multiple DMD projectors , 2019, OPTO.

[30]  Suming Tang,et al.  Micro-phase measuring profilometry: Its sensitivity analysis and phase unwrapping , 2015 .

[31]  Ines Ernst,et al.  Mutual Information Based Semi-Global Stereo Matching on the GPU , 2008, ISVC.

[32]  Ashok Veeraraghavan,et al.  A Practical Approach to 3D Scanning in the Presence of Interreflections, Subsurface Scattering and Defocus , 2013, International Journal of Computer Vision.