Fast 3D Measurement through Windowed Space-Time Analysis

Abstract

Abstract: Space-time analysis is one of the most robust methods to measure 3D shape. However, it has some disadvantages, such as large storage burden, high computational load, long waiting time. A fast space-time analysis was proposed which applies the windowed data instead of the whole time series data. The new method greatly reduces the computational load, storage burden and waiting time. The experiments on three test objects were implemented. The surfaces using the proposed method are very fine, even the small texture and wave on the surface are also reconstructed. In the quantitative comparison with the traditional spacetime analysis, the accuracies and recalls are as high as 99.8%. what is move, the efficiency is enhanced rapidly. The computational time is just 2% of the traditional one. The experiment results confirm that the windowed space-time analysis can obtain high quality computing result while using very low computing cost.

Key words: space-time analysis, three dimensional measurement； Gaussian filter

CLC Number:

TN 206

ZHANG Xu-1, ZHU Li-Min-2. Fast 3D Measurement through Windowed Space-Time Analysis[J]. Journal of Shanghai Jiaotong University, 2012, 46(06): 859-864.

References

［1］Chen F, Brown G, Song M. Overview of threedimensional shape measurement using optical methods［J］. Optical Engineering, 2000. 39(1):1022.

［2］Blais F. Review of 20 years of range sensor development［J］. Journal of Electronic Imaging, 2004, 13(1):231240.

［3］Salvi J, Fernandez S, Pribanic T, et al. A state of the art in structured light patterns for surface profilometry［J］. Pattern Recognition, 2010, 43(8):26662680.

［4］Sato K, Inokuchi S. Threedimensional surface measurement by space encoding range imaging［J］. Journal of Robotic Systems, 1985, 2(1): 2739.

［5］Zhang S. Recent progresses on realtime 3D shape measurement using digital fringe projection techniques［J］. Optics and Lasers in Engineering, 2010 48 (2):149158.

［6］Salvi J, Batlle J, Mouaddib E. A robustcoded pattern projection for dynamic 3D scene measurement［J］. Pattern Recognition Letters,1998,19 (11): 10551065.

［7］Petriu E, Bieseman T, Trif N. et al. Visual object recognition using pseudorandom grid encoding［C］// Proceedings of the 1992 lEEE/RSJ International Conference on Intelligent Robots and Systems. Raleigh,NC: IEEE,1992: 16171624.

［8］Boyer K, Kak A. Colorencoded structured light for rapid active ranging［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(1):1428.

［9］Hügli H,Maitre G. Generation and use of color pseudo random sequences for coding structured light in active ranging［C］//Society of Photooptical Instrumentation Engineers Conference Series. Hamburg: SPIE,1989: 7582.

［10］Chen C, Hung Y, Chiang C, et al. Range data acquisition using color structured lighting and stereo vision ［J］. Image and Vision Computing, 1997,15(6): 445456.

［11］Zhang L, Curless B, Seitz S. Rapid shape acquisition using color structured light and multipass dynamic programming［C］//The 1st IEEE International Symposium on 3D Data Processing, Visualization, and Transmission. Padova, Italy: IEEE, 2002: 2436.

［12］Koninckx T, Geys I, Jaeggli T, et al. A graph cut based adaptive structured light approach for realtime range acquisition［C］//International Symposium on 3D Data Processing, Visualization and Transmission. Thessaloniki, Greece: IEEE,2004:413421.

［13］Zhang X, Zhu L M, Li Y F. Indirect decoding edges for oneshot shape acquisition［J］. Journal of the Optical Society of America A, 2011, 28(4):651661.

［14］Zhang X, Zhu L M. Determination of edge correspondence using color codes for oneshot shape acquisition［J］. Optics and Lasers in Engineering, 2011, 49(1):97103.

［15］Zhang X, Zhu L M, Chu L W. Evaluation of coded structured light methods using ground truth［C］//IEEE International Conference on Cybernetics and Intelligent Systems. Qingdao, China: IEEE, 2011:117123.

［16］Curless B, Levoy M. Better optical triangulation through spacetime analysis［C］//Proceedings of IEEE International Conference on Computer Vision. Cambridge, MA: IEEE, 1995:987994.