Boundary Inheritance Codec for high-accuracy structured light three-dimensional reconstruction with comparative performance evaluation.

This paper presents a new method of structured light-based 3D reconstruction, referred to here as Boundary Inheritance Codec, that provides high accuracy and low noise in projector-camera correspondence. The proposed method features (1) real-boundary recovery: the exact locations of region boundaries, defined by a coded pattern, are identified in terms of their real coordinates on the image plane. To this end, a radiance independent recovery of accurate boundaries and a disambiguation of true and false boundaries are presented. (2) Boundary inheritance: the consistency among the same boundaries of different layers in pattern hierarchy is exploited to further enhance the accuracy of region correspondence and boundary estimation. Extensive experimentations are carried out to verify the performance of the proposed Boundary Inheritance Codec, especially, in comparison with a number of well-known methods currently available, including Gray-code (GC) plus line/phase shift (LS/PS). The results indicate that the proposed method of recovering real boundaries with boundary inheritance is superior in accuracy and robustness to Gray-code inverse (GCI), GC+LS/PS. For instance, the error standard deviation and the percentile of outliers of the proposed method were 0.152 mm and 0.089%, respectively, while those of GCI were 0.312 mm and 3.937%, respectively, and those of GC+LS/PS were 0.280/0.321 mm and 0.159/7.074%, respectively.

[1]  Jens Guehring,et al.  Dense 3D surface acquisition by structured light using off-the-shelf components , 2000, IS&T/SPIE Electronic Imaging.

[2]  S. Inokuchi,et al.  Range-imaging system for 3-D object recognition , 1984 .

[3]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[4]  L. Lucy An iterative technique for the rectification of observed distributions , 1974 .

[5]  Lam Quang Bui,et al.  Accurate estimation of the boundaries of a structured light pattern. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Giovanna Sansoni,et al.  OPL-3D: A novel, portable optical digitizer for fast acquisition of free-form surfaces , 2003 .

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

[8]  Sooyong Park,et al.  Service robot for the elderly , 2009, IEEE Robotics & Automation Magazine.

[9]  Dongliang Zheng,et al.  Self-correction phase unwrapping method based on Gray-code light , 2012 .

[10]  Song Zhang Recent progresses on real-time 3D shape measurement using digital fringe projection techniques , 2010 .

[11]  Sukhan Lee,et al.  Signal Separation Coding for Robust Depth Imaging Based on Structured Light , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Qi Hao,et al.  Dual-frequency pattern scheme for high-speed 3-D shape measurement. , 2010, Optics express.

[13]  M. Trobina Error Model of a Coded-Light Range Sensor , 2007 .

[14]  Jeffrey L. Posdamer,et al.  Surface measurement by space-encoded projected beam systems , 1982, Comput. Graph. Image Process..

[15]  Georg Wiora High-resolution measurement of phase-shift amplitude and numeric object phase calculation , 2000, SPIE Optics + Photonics.

[16]  François Blais Review of 20 years of range sensor development , 2004, J. Electronic Imaging.