伪随机编码结构光三维重构及标定算法研究
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摘要
本文主要围绕结构光系统的标定和三维重构进行了研究和探讨。在结构光系统标定阶段,提出了直接用电脑显示器对结构光系统进行标定的方法,相比传统的用纸打印再张贴的方法,因为消去了张贴不平整引起的误差,即使只用到较少的图像,在这种特定的情况下精度能提高五到十倍。在结构光系统三维重构阶段,采用一种结合伪随机阵列的彩色菱形模式,并将菱形之间的交点选为特征点,将特征点根据其领域有无颜色差别分为两类,再结合二维伪随机阵列的性质,这样每个特征点就有了唯一的编码,在解码阶段,提出了一种基于菱形双重对称性的检测算子,独立地检测各个特征点,并以亚像素级的精度确定特征点。实验结果表明,在这种曲面变形情况下检测特征点,相比Harris算子以及一般的十字形检测模板有更好的定位检测效果。
本论文主要有以下几个方面的内容:1)介绍了结构光三维重构系统的发展历程和现状以及应用前景。2)结构光系统标定常用的方法和步骤以及本文采用的电脑显示器标定方法,并列出了求解过程中用到的数学思想以及程序实现流程图,最后比较和分析实验结果。3)结构光系统编解码阶段的常用方法分三类,分别是基于时域编码、基于空域编码、直接编解码,对这些方法对了小结,列出了本文采用的投影模式以及解码方法,最后结合标定得到的内外参数结果,利用三角测量学的原理,计算出物体表面的深度信息,从而重构物体表面,并通过matlab实验显示了重构的结果。
The main work of this paper is about the calibration of structured-light system and 3D reconstruction strategy. In the calibration phase of structured-light system, we proposed a method that using a computer monitor to calibrate the system. Because of the errors caused by paste in the calibration plane eliminated, although just using fewer images, but we can get a higher precision. At last, the calibration precision almost improved five to ten times. In the 3D reconstruction phase of structured-light system, a 2D pseudo-random pattern consisting of rhombic color elements is used, and the grid-points between the pattern elements are selected as the feature point. We describe how a classification of the grid-point types plus the pseudo-randomness of the projected pattern can equip each grid-point with a unique label that is preserved in the captured image. We also present a grid-point detector that determines the grid-points independently of one another, and that localizes the grid-points in sub-pixel accuracy. The detector is based upon certain symmetry that the grid-points of rhombic pattern own the 2-fold rotation symmetry. Extensive experiments are presented to illustrate how the proposed system performs in comparison with those that are based upon classical feature detectors like Harris, and the other template-based feature detectors.
The main content of this paper contains as follows: 1) the evolution process of structured-light system and the present and potential applications were introduced. 2) The traditional method and process of calibration were contained. We used a computer monitor to calibrate the structured-light system and introduced the mathematical method and program flowchart in the calculation. After that, we also made comparison between the experimental results with the traditional method. 3)In the encoding phase, we introduced the main method in this structure-light aspect and divided method into three categories, which are time-multiplexing strategy, spatial neighborhood and direct codification. The projection pattern and encoding strategy in this paper also contained. We used the intrinsic and extrinsic parameters result of the last calibration step to calculate the depth information of the object surface, so that we can reconstruct the object surface. We have done the experiment in matlab and displayed the 3D reconstruction results.
本论文主要有以下几个方面的内容:1)介绍了结构光三维重构系统的发展历程和现状以及应用前景。2)结构光系统标定常用的方法和步骤以及本文采用的电脑显示器标定方法,并列出了求解过程中用到的数学思想以及程序实现流程图,最后比较和分析实验结果。3)结构光系统编解码阶段的常用方法分三类,分别是基于时域编码、基于空域编码、直接编解码,对这些方法对了小结,列出了本文采用的投影模式以及解码方法,最后结合标定得到的内外参数结果,利用三角测量学的原理,计算出物体表面的深度信息,从而重构物体表面,并通过matlab实验显示了重构的结果。
The main work of this paper is about the calibration of structured-light system and 3D reconstruction strategy. In the calibration phase of structured-light system, we proposed a method that using a computer monitor to calibrate the system. Because of the errors caused by paste in the calibration plane eliminated, although just using fewer images, but we can get a higher precision. At last, the calibration precision almost improved five to ten times. In the 3D reconstruction phase of structured-light system, a 2D pseudo-random pattern consisting of rhombic color elements is used, and the grid-points between the pattern elements are selected as the feature point. We describe how a classification of the grid-point types plus the pseudo-randomness of the projected pattern can equip each grid-point with a unique label that is preserved in the captured image. We also present a grid-point detector that determines the grid-points independently of one another, and that localizes the grid-points in sub-pixel accuracy. The detector is based upon certain symmetry that the grid-points of rhombic pattern own the 2-fold rotation symmetry. Extensive experiments are presented to illustrate how the proposed system performs in comparison with those that are based upon classical feature detectors like Harris, and the other template-based feature detectors.
The main content of this paper contains as follows: 1) the evolution process of structured-light system and the present and potential applications were introduced. 2) The traditional method and process of calibration were contained. We used a computer monitor to calibrate the structured-light system and introduced the mathematical method and program flowchart in the calculation. After that, we also made comparison between the experimental results with the traditional method. 3)In the encoding phase, we introduced the main method in this structure-light aspect and divided method into three categories, which are time-multiplexing strategy, spatial neighborhood and direct codification. The projection pattern and encoding strategy in this paper also contained. We used the intrinsic and extrinsic parameters result of the last calibration step to calculate the depth information of the object surface, so that we can reconstruct the object surface. We have done the experiment in matlab and displayed the 3D reconstruction results.
引文
[1]孙宇臣.物体三维信息测量技术综述[J].光电子激光. 2004,15(2):248-254
[2]孙亦南,刘伟军,王超越.由序列图像进行三维测量的新方法[J].光电工程. 2005,32(7):59-63
[3]陶立.基于结构光扫描彩色三维信息测量技术[J].光电子激光. 2006,0l:134-137
[4]虞启琏,张天虎.利用结构光进行三维测量的新方法[J].应用光学. 2000,01:23-27
[5]徐智华.计算机视觉中实现图像三维重构的方法研究[J].福建广播电视大学学报. 2008(3):68:70
[6]刘洋.基于编码结构光的三维扫描仪原型系统研发[D].浙江:浙江大学,2005
[7]薛婷,叶声华.基于线结构光视觉传感器的圆孔定位误差分析[J].光学精密工程. 2008(4):23-28
[8]潘伟,赵毅,阮雪榆.采用光栅投影的三维测量方法[J].光电工程. 2003(02): 34-38
[9] Joaquim Salvi, Jordi Pages,Joan Batlle. Pattern codification strategies in structured light systems[J]. Pattern Recognition. 2003:827-849
[10] M.Ribo, M.Brandner. State of the art on vision-based structured light systems for 3D measurements[C] . Proc IEEE Workshop Robot. 2005:2–6
[11]马颂德,张正友.计算机视觉[M].北京:科学出版社,1997:52-71
[12] R.Tsai. A versatile camera calibration technique for high accuracy 3-d machine vision Using Off-the-shelf TV Cameras and Lenses. ICCV,1987:23-45
[13] Zhengyou Zhang. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000,22(11):1330- 1334
[14] P. Sturm , S. Maybank. On plane-based camera calibration: A general algorithm, singularities, applications[C]. Proc IEEE CVPR. 1999:432–437
[15] G. Sansoni, M. Carocci, R. Rodella. Calibration and performance evaluation of a 3-D imaging sensor based on the projection of structured light[J]. IEEE TransInstrum. 2000,49(3):628–636
[16] F.Sadlo, T.Weyrich. A practical structured light acquisition system for point-based geometry and texture[C]. Proc Eurographics Symp. 2005:89–98.
[17] R. Legarda-Sáenz, T. Bothe, W. P. Jüptner. Accurate procedure for the calibration of a structured light system. Opt Eng. 2004,43(2):464–471
[18] S.Zhang, P.S.Huang. Novel method for structured light system calibration [J]. Opt.Eng. 2006,l45(8):083-601
[19] D.Fofia, J.Salvib, E.M.Mouaddib. Uncalibrated reconstruction:An adaptation to structured light vision[J]. Pattern Recognition. 2003,36(7): 1631–1644
[20] Y. F. Li, S. Y. Chen. Automatic recalibration of an active structured-light vision system[C]. IEEE Trans Robot. Autom. 2003,19(2):259–268
[21] Y. I. Abdel-aziz ,H. M. Karara. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. 1971: 23-45
[22] J.Heikkil, O.Silvén. A four-step camera calibration procedure with implicit image correction[C]. Proc IEEE CVPR. 1997:1106–1112
[23] R.Lenz, R.Tsai. Techniques for calibration of the scale factorand image center for high accuracy 3-D machine vision metrology[J]. IEEE PAMI. 1988,10(5):713–720
[24] J.Weng, P.Cohen, M.Herniou. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Trans,PAMI. 1992,14(10):965–980
[25] M. Maruyama, S. Abe. Range sensing by projecting multiple slits with random cuts[J]. PAMI. 1993,15 (6):647–651
[26] M. Ito, A. Ishii. A three-level checkerboard pattern projection method for curved surface measurement[J]. Pattern Recognition. 1995,28(1):27–40
[27] N.G. Durdle, J. Thayyoor, V.J. Raso. An improved structured light technique for surface reconstruction of the human trunk[C]. IEEE Canadian Conference on Electrical and Computer Engineering. 1998,2:874–877
[28] K.L. Boyer, A.C. Kak. Color-encoded structured light for rapid active ranging [J]. IEEE Trans,PAMI. 1987,9(1):14–28
[29] C.Chen, Y.Hung, C.Chiang, J. Wu. Range data acquisition using color structuredlighting and stereo vision[J]. Image Vision Comput. 1997(15):445–456
[30] MacWilliams F.J, Sloane N.J.A. Pseudo-random sequences and arrays[J]. Proc. IEEE. 1976,64 (12):1715–1729
[31] H.HQugli, G. MaQitre. Generation and use of color pseudo random sequences for coding structured light in active ranging[C]. Proceedings of Industrial Inspection. 1989,1010:75–82
[32] T.Monks,J.Carter. Improved stripe matching for colour encoded structured light [C]. Fifth International Conference on Computer Analysis of Images and Patterns. 1993:476–485
[33] P. Vuylsteke, A. Oosterlinck. Range image acquisition with a single binary encoded light pattern[J]. PAMI. 1990,12(2):148–163
[34] T. Pajdla. Bcrf—binary-coded illumination range finder reimplementation [C].Technical Report KUL/ESAT/MI2/9502,Katholieke Universiteit Leuven, April 1995,ESAT, Kardinaal Mercierlaan 94:B-3001
[35] J. Salvi, J. Batlle, E. Mouaddib. A robust-coded pattern projection for dynamic 3d scene measurement[J]. Pattern Recognition Lett. 1998,(19):1055–1065
[36] P. Lavoie, D. Ionescu, E. Petriu. A high precision 3D object reconstruction method using a color coded grid and nurbs[C]. Proceedings of the International Conference on Image Analysis and Processing. 1999:370–375
[37] E.M. Petriu, Z. Sakr, S, A. Moica. Object recognition using pseudorandom color encoded structured light[C]. Proceedings of the 17th IEEE Instrumentation and Measurement technology Conference. 2000,3:1237-1241
[38]马世伟,陈辉,许桢英.彩色伪随机编码图像角点检测方法研究[J].计量学报. 2008,29(4):24-27
[39]姜晨,卢荣胜,张勇斌,宫能刚.彩色伪随机阵列在主动三维视觉检测中的应用[J].光电工程. 2002,12:141-144
[40]廖素引,金施群,马修水.彩色伪随机编码在特征点匹配中的应用研究[J].计量测试与检定. 2005,15(6):16-18
[41] Harris C G, Stephens. A Combined Corner and Edge Detector[C]. Proceedings of 4th Alvey Vision Conference. 1988
[42]姜晨,卢荣胜,张勇斌.伪随机阵列和Harris角测量在视觉检测中的应用[J].计量测试与检定. 2005,15(4):10-13
[43] S.M.Smith. Susan-a new approach to low level image processing[J]. Technical Report TR95SMS1c. 1995
[44]余志波.数字图像处理与Matlab实现[M].重庆大学出版社,2002:46-232
[45]周爽. SAS数据挖掘与分析[M].清华大学出版社,2007:98-103
[2]孙亦南,刘伟军,王超越.由序列图像进行三维测量的新方法[J].光电工程. 2005,32(7):59-63
[3]陶立.基于结构光扫描彩色三维信息测量技术[J].光电子激光. 2006,0l:134-137
[4]虞启琏,张天虎.利用结构光进行三维测量的新方法[J].应用光学. 2000,01:23-27
[5]徐智华.计算机视觉中实现图像三维重构的方法研究[J].福建广播电视大学学报. 2008(3):68:70
[6]刘洋.基于编码结构光的三维扫描仪原型系统研发[D].浙江:浙江大学,2005
[7]薛婷,叶声华.基于线结构光视觉传感器的圆孔定位误差分析[J].光学精密工程. 2008(4):23-28
[8]潘伟,赵毅,阮雪榆.采用光栅投影的三维测量方法[J].光电工程. 2003(02): 34-38
[9] Joaquim Salvi, Jordi Pages,Joan Batlle. Pattern codification strategies in structured light systems[J]. Pattern Recognition. 2003:827-849
[10] M.Ribo, M.Brandner. State of the art on vision-based structured light systems for 3D measurements[C] . Proc IEEE Workshop Robot. 2005:2–6
[11]马颂德,张正友.计算机视觉[M].北京:科学出版社,1997:52-71
[12] R.Tsai. A versatile camera calibration technique for high accuracy 3-d machine vision Using Off-the-shelf TV Cameras and Lenses. ICCV,1987:23-45
[13] Zhengyou Zhang. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000,22(11):1330- 1334
[14] P. Sturm , S. Maybank. On plane-based camera calibration: A general algorithm, singularities, applications[C]. Proc IEEE CVPR. 1999:432–437
[15] G. Sansoni, M. Carocci, R. Rodella. Calibration and performance evaluation of a 3-D imaging sensor based on the projection of structured light[J]. IEEE TransInstrum. 2000,49(3):628–636
[16] F.Sadlo, T.Weyrich. A practical structured light acquisition system for point-based geometry and texture[C]. Proc Eurographics Symp. 2005:89–98.
[17] R. Legarda-Sáenz, T. Bothe, W. P. Jüptner. Accurate procedure for the calibration of a structured light system. Opt Eng. 2004,43(2):464–471
[18] S.Zhang, P.S.Huang. Novel method for structured light system calibration [J]. Opt.Eng. 2006,l45(8):083-601
[19] D.Fofia, J.Salvib, E.M.Mouaddib. Uncalibrated reconstruction:An adaptation to structured light vision[J]. Pattern Recognition. 2003,36(7): 1631–1644
[20] Y. F. Li, S. Y. Chen. Automatic recalibration of an active structured-light vision system[C]. IEEE Trans Robot. Autom. 2003,19(2):259–268
[21] Y. I. Abdel-aziz ,H. M. Karara. Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. 1971: 23-45
[22] J.Heikkil, O.Silvén. A four-step camera calibration procedure with implicit image correction[C]. Proc IEEE CVPR. 1997:1106–1112
[23] R.Lenz, R.Tsai. Techniques for calibration of the scale factorand image center for high accuracy 3-D machine vision metrology[J]. IEEE PAMI. 1988,10(5):713–720
[24] J.Weng, P.Cohen, M.Herniou. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Trans,PAMI. 1992,14(10):965–980
[25] M. Maruyama, S. Abe. Range sensing by projecting multiple slits with random cuts[J]. PAMI. 1993,15 (6):647–651
[26] M. Ito, A. Ishii. A three-level checkerboard pattern projection method for curved surface measurement[J]. Pattern Recognition. 1995,28(1):27–40
[27] N.G. Durdle, J. Thayyoor, V.J. Raso. An improved structured light technique for surface reconstruction of the human trunk[C]. IEEE Canadian Conference on Electrical and Computer Engineering. 1998,2:874–877
[28] K.L. Boyer, A.C. Kak. Color-encoded structured light for rapid active ranging [J]. IEEE Trans,PAMI. 1987,9(1):14–28
[29] C.Chen, Y.Hung, C.Chiang, J. Wu. Range data acquisition using color structuredlighting and stereo vision[J]. Image Vision Comput. 1997(15):445–456
[30] MacWilliams F.J, Sloane N.J.A. Pseudo-random sequences and arrays[J]. Proc. IEEE. 1976,64 (12):1715–1729
[31] H.HQugli, G. MaQitre. Generation and use of color pseudo random sequences for coding structured light in active ranging[C]. Proceedings of Industrial Inspection. 1989,1010:75–82
[32] T.Monks,J.Carter. Improved stripe matching for colour encoded structured light [C]. Fifth International Conference on Computer Analysis of Images and Patterns. 1993:476–485
[33] P. Vuylsteke, A. Oosterlinck. Range image acquisition with a single binary encoded light pattern[J]. PAMI. 1990,12(2):148–163
[34] T. Pajdla. Bcrf—binary-coded illumination range finder reimplementation [C].Technical Report KUL/ESAT/MI2/9502,Katholieke Universiteit Leuven, April 1995,ESAT, Kardinaal Mercierlaan 94:B-3001
[35] J. Salvi, J. Batlle, E. Mouaddib. A robust-coded pattern projection for dynamic 3d scene measurement[J]. Pattern Recognition Lett. 1998,(19):1055–1065
[36] P. Lavoie, D. Ionescu, E. Petriu. A high precision 3D object reconstruction method using a color coded grid and nurbs[C]. Proceedings of the International Conference on Image Analysis and Processing. 1999:370–375
[37] E.M. Petriu, Z. Sakr, S, A. Moica. Object recognition using pseudorandom color encoded structured light[C]. Proceedings of the 17th IEEE Instrumentation and Measurement technology Conference. 2000,3:1237-1241
[38]马世伟,陈辉,许桢英.彩色伪随机编码图像角点检测方法研究[J].计量学报. 2008,29(4):24-27
[39]姜晨,卢荣胜,张勇斌,宫能刚.彩色伪随机阵列在主动三维视觉检测中的应用[J].光电工程. 2002,12:141-144
[40]廖素引,金施群,马修水.彩色伪随机编码在特征点匹配中的应用研究[J].计量测试与检定. 2005,15(6):16-18
[41] Harris C G, Stephens. A Combined Corner and Edge Detector[C]. Proceedings of 4th Alvey Vision Conference. 1988
[42]姜晨,卢荣胜,张勇斌.伪随机阵列和Harris角测量在视觉检测中的应用[J].计量测试与检定. 2005,15(4):10-13
[43] S.M.Smith. Susan-a new approach to low level image processing[J]. Technical Report TR95SMS1c. 1995
[44]余志波.数字图像处理与Matlab实现[M].重庆大学出版社,2002:46-232
[45]周爽. SAS数据挖掘与分析[M].清华大学出版社,2007:98-103