基于多视相移框架的动态物体三维面形测量技术与系统研究
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摘要
三维面形测量技术目前已在航空航天、武器装备、汽车等领域得到了广泛应用,这些领域的核心零部件(如发动机缸体缸盖、叶轮叶片、进排气管道等)大多结构复杂、曲面不规则,在其设计制造过程中,采用三维面形测量技术不仅能够充分利用现有的设计制造成果,提高设计速度、缩短开发周期；还可精确控制产品质量,降低生产成本。然而,现有的三维面形测量技术大多只能测量静态物体,对于诸如叶片旋转过程中的动态三维形变、飞行器风洞实验中关键部件的动态三维形变、机车冲撞实验中机车表面连续变化的三维形貌和电磁成形过程中板料的连续三维形变等动态过程尚缺少有效的实时三维面形测量方法。这些动态过程的连续三维面形测量数据可与计算机模拟结果进行对比,以验证模拟结果的正确性,并有助于描绘和分析物体运动过程中表面形态的变化规律,为研究变形机理和改进相关工艺参数提供基础数据,具有重要的理论和应用价值。
本文针对基于多视相移框架的动态物体三维面形测量技术中的系统非线性误差校正、基于多视相移框架的对应点查找、高精度的多视系统标定和大规模细粒度并行计算等关键技术的研究基础上,通过系统硬件精确同步采集设计与软件设计,开发一套全时间分辨率、全空间分辨率不连续具有任意复杂形状的动态物体三维测量系统。
在以三步相移技术为基础的动态物体三维面形测量系统中,相位计算精度容易受到系统非线性响应的影响,使测量结果产生较大的周期性误差。为了提高三步相移的计算精度,对系统的非线性响应进行了系统的建模。该模型充分考虑了投影仪空间离焦现象的影响,能准确地揭示光栅图像空间非正弦化的规律。在此基础之上,提出了一种系统非线性响应伽马参数标定算法,该算法将标定的伽马值预先调制到预投影的光栅图像中,间接的对相位误差进行补偿。实验结果表明,该方法能将最大相位误差控制在0.045rad,从而有效地抑制由系统非线性响应引起的周期性测量误差,并且为整个三维计算过程打下了良好的基础。
实现全时间分辨率、全空间分辨率任意复杂形状物体三维面形测量在科学研究与工程应用中具有重要的意义。为此,提出一种基于多视相移框架的对应点查找方法,该方法根据多视约束与相移原理,使得每个像素在解相完成后可以独立进行图像匹配与三维重构,无需相位展开过程。因此可以使测量系统实现任意复杂形状物体的全空间分辨率的三维测量。在连续测量的图像数据流中,任意三幅图像都可以用来重构三维数据,所以三维测量速度可以和相机采集速度一致,从而实现全时间分辨率的动态三维面形测量。计算过程中,根据相位图快速、稳定的图像匹配是保证动态物体三维面形测量稳定性的基础。匹配过程中,多光栅数与相位误差很容易导致误匹配,为此提出一种立体匹配两步方法(包括视差范围约束与混合一致性检查)来去除误匹配点,从而保证了图像匹配的正确率。此外,为了使匹配精度对相位误差不敏感,采用边界点相位校正来辅助边界点的对应点查找,同时利用极线约束对正确的匹配点进行精度优化。
系统标定的精度直接决定了三维面形测量的精度与稳定性。为此,提出一种高精度的多目视觉标定方法。该方法主要包括单相机的内部参数标定与多相机间的位姿关系标定。针对单相机标定,采用基于平面标靶的柔性标定方法,得到相机参数的初值,然后利用光束法平差策略对相机的标定参数与标靶特征的三维坐标进行同时优化,进而消除标靶误差对标定结果的影响。针对多相机间的位姿关系标定,将世界坐标系固定在某一个相机上,利用两两相机间的极线约束建立目标方程,进而优化得到相机间的位姿关系,最后对位姿参数的平移矩阵中的比例因子进行修正。此外,系统在使用过程中,相机间的位姿关系容易受环境或人为因素影响,从而发生改变。为了保证系统的测量精度。在现有位姿标定方法基础上,研究了一种增强的相机姿态快速标定方法。该方法只要求相机拍摄一幅标靶图像,便能准确的计算出相机的位姿参数。实验证明,以上标定方法在噪声环境下具有良好的标定精度与稳定性。
大数据量的密集计算一直是实时动态三维测量的瓶颈。为了有效提高三维计算速度,采用基于大规模、细粒度并行计算模型,充分利用GPU并行计算的性能优势,设计出应用在常规主流芯片上的高分辨率、高精度、实时动态三维测量并行算法。计算测试表明,GPU中三维计算的运行效率比在CPU中提高400倍左右,并实现每毫秒32000个点的实时动态三维计算,从而为实时动态物体三维面形测量技术的工程应用提供较为重要的基础。
在前述理论与技术研究的基础上,研制一套全时间分辨率、全空间分辨率具有任意复杂形状的动态物体三维面形测量系统设备。硬件方面,设计了投影仪与相机的同步时序。针对其中的DMD翻转延时与相机接受到触发信号至感光响应的延时,设计两组测试实验分别测定了延时的大小,实现投影仪与相机精确同步,实验表明该系统能实现每秒220帧的精确同步采集。软件方面,根据前述的关键理论与技术开发出动态物体三维面形测量软件系统,实现了每秒220帧三维面形测量,测量分辨率为1024×768,并且探索了该系统在材料变形、运动追踪、人脸测量和手势与姿态识别中的应用前景。
3D shape measurement technology has been widely used in the aerospace, weapons, automobile and other fields. The shape of key parts of these areas (such as the engine block, blades, exhaust pipes, etc.) is usually very complex, and the3D shape measurement technology not only be able to take full advantage of the existing design to improve the design speed and shorten the development cycle, but also control the product quality and reduce production costs. However, most of the existing3D shape measurement techniques can only measure the static objects. Dynamic3D measurement is increasingly important in many applications such as measuring the deformation of the rotating blades, the aircraft wind tunnel experiment, collision experiments and electromagnetic forming process. The dynamic process of continuous3D shape measurement results can be used to verify the computer simulation results, help to delineate and analyze the variation of the surface morphology and improve manufacture process, so it is very important in theoretical and practical researches.
This paper presents a novel sinusoidal grating projection-based dynamic3D measurement techniques based on the development of nonlinear error correction technique, multi-view phase-shift framework based dynamic3D measurement, high-precision multi-view system calibration and massive fine-grained parallel computing. By accurate synchronization acquisition hardware design and software design, a high accurate, full temporal and spatial resolution, dynamic3D measurement system is developed.
Typically, the phase value accuracy calculated by the three-step phase-shift algorithm will be influenced by the non-linear response of the system. In order to improve phase calculation accuracy, the nonlinear response of the measurement system is modeled. Compared with the existing models, it takes the defocus of projector into consideration and can describe the non-linear distortion of the fringe images in space accurately. On this basis, a gamma parameter calibration algorithm is proposed, and the calibrated gamma value will be encoded into the fringe images before measurement task for phase error compensation. Experimental results showed that the phase error could be control within0.045rad, and the3D measurement error was effectively suppressed, and it laid a good foundation for3D calculation process.
It is very important to achieve full temporal and spatial resolution3D measurement of arbitary shape objects in scientific research and engineering application. So a stereo matching method based on multi-view phase-shift framework is proposed for dynamic3D measurement. In this framework, benefited from the trifocal tensor constraint, each pixel can explore its corresponding point independently only in the wrapped phase-map and reconstruct a3D point in space, so arbitrary shape including the discontinuous surfaces or step-like surfaces can be measured with full spatial resolution. Moreover, as any adjacent three images per camera in the continuous capturing stream can be used to reconstruct one3D frame, the3D acquisition speed can be as fast as the camera capturing speed. So our method can also perform full temporal resolution3D measurements. To enhance the resistance to false correspondence caused by multiple fringe number and phase error, a two-stage strategy, using disparity range constraint and mixed consistency check successively, is proposed to reject the wrong candidates. Further, the accuracy of the corresponding points is refined by performing edge point adjustment and correspondence refinement, and makes the3D result insensitive to the phase error.
System calibration accuracy is very critical to the measurement accuracy, and a multi-view system calibration method is proposed. The calibration procedure contains two steps:(1) calibrating the internal parameters of each camera individually (2) determining the pose of each camera. For single camera calibration problem, Zhang's flexible calibration method is used to obtain the initial values, and these values are optimized by the bundle adjustment strategy to eliminate the influence of the inaccuracy of the target. Then the world coordinate is located on the camera coordinate, the pose of each camera is calibrated by epipolar constraint, and the calibrated translation vector has to be revised to remove the scaling factor. In addition, due to the environmental factors, the pose of the camera might slightly change, which will make the measurement result inaccurate. In order to grarantee the measurement accuracy, a fast and enhanced pose calibration method is used to determining the pose of each camera. Experimental results showed that this method is accurate and stable.
The large amount of data intensive computing is always the bottleneck of real-time dynamic3D measurement. In order to effectively improve the speed of3D calculation, a large-scale, fine-grained parallel computing model, which take full advantage of the performance of GPU parallel computing, is used to design a high-resolution high-precision, real-time dynamic3D parallel computing algorithms in the conventional mainstream chip. The experiment showed that the computation efficiency in the GPU is nearly400times faster than that in the CPU. And it achieved real-time calculation of32000points per millisecond, which will provide an important foundation in real-time3D measurement applications.
On the basis of the above theory and technology researches, a dynamic3D shape measurement equipment is developed. In this equipment, the synchronous timing of the projector with the camera is designed to correctly capture gray scale images. Two test experiments are designed to determine the time delay of DMD flip and time delay of camera receiving a trigger signal and the photosensitive response. The experiments show that the system can achieve precise synchronization acquisition of220per second. On the other hand, a dynamic3D measurement software system is designed to achieve2203D frames per second dynamic measurement with spatial resolution of1024x768. Finally application prospects of this system in material deformation, motion tracking, the face measuring and gestures and gesture recognition is also described.
本文针对基于多视相移框架的动态物体三维面形测量技术中的系统非线性误差校正、基于多视相移框架的对应点查找、高精度的多视系统标定和大规模细粒度并行计算等关键技术的研究基础上,通过系统硬件精确同步采集设计与软件设计,开发一套全时间分辨率、全空间分辨率不连续具有任意复杂形状的动态物体三维测量系统。
在以三步相移技术为基础的动态物体三维面形测量系统中,相位计算精度容易受到系统非线性响应的影响,使测量结果产生较大的周期性误差。为了提高三步相移的计算精度,对系统的非线性响应进行了系统的建模。该模型充分考虑了投影仪空间离焦现象的影响,能准确地揭示光栅图像空间非正弦化的规律。在此基础之上,提出了一种系统非线性响应伽马参数标定算法,该算法将标定的伽马值预先调制到预投影的光栅图像中,间接的对相位误差进行补偿。实验结果表明,该方法能将最大相位误差控制在0.045rad,从而有效地抑制由系统非线性响应引起的周期性测量误差,并且为整个三维计算过程打下了良好的基础。
实现全时间分辨率、全空间分辨率任意复杂形状物体三维面形测量在科学研究与工程应用中具有重要的意义。为此,提出一种基于多视相移框架的对应点查找方法,该方法根据多视约束与相移原理,使得每个像素在解相完成后可以独立进行图像匹配与三维重构,无需相位展开过程。因此可以使测量系统实现任意复杂形状物体的全空间分辨率的三维测量。在连续测量的图像数据流中,任意三幅图像都可以用来重构三维数据,所以三维测量速度可以和相机采集速度一致,从而实现全时间分辨率的动态三维面形测量。计算过程中,根据相位图快速、稳定的图像匹配是保证动态物体三维面形测量稳定性的基础。匹配过程中,多光栅数与相位误差很容易导致误匹配,为此提出一种立体匹配两步方法(包括视差范围约束与混合一致性检查)来去除误匹配点,从而保证了图像匹配的正确率。此外,为了使匹配精度对相位误差不敏感,采用边界点相位校正来辅助边界点的对应点查找,同时利用极线约束对正确的匹配点进行精度优化。
系统标定的精度直接决定了三维面形测量的精度与稳定性。为此,提出一种高精度的多目视觉标定方法。该方法主要包括单相机的内部参数标定与多相机间的位姿关系标定。针对单相机标定,采用基于平面标靶的柔性标定方法,得到相机参数的初值,然后利用光束法平差策略对相机的标定参数与标靶特征的三维坐标进行同时优化,进而消除标靶误差对标定结果的影响。针对多相机间的位姿关系标定,将世界坐标系固定在某一个相机上,利用两两相机间的极线约束建立目标方程,进而优化得到相机间的位姿关系,最后对位姿参数的平移矩阵中的比例因子进行修正。此外,系统在使用过程中,相机间的位姿关系容易受环境或人为因素影响,从而发生改变。为了保证系统的测量精度。在现有位姿标定方法基础上,研究了一种增强的相机姿态快速标定方法。该方法只要求相机拍摄一幅标靶图像,便能准确的计算出相机的位姿参数。实验证明,以上标定方法在噪声环境下具有良好的标定精度与稳定性。
大数据量的密集计算一直是实时动态三维测量的瓶颈。为了有效提高三维计算速度,采用基于大规模、细粒度并行计算模型,充分利用GPU并行计算的性能优势,设计出应用在常规主流芯片上的高分辨率、高精度、实时动态三维测量并行算法。计算测试表明,GPU中三维计算的运行效率比在CPU中提高400倍左右,并实现每毫秒32000个点的实时动态三维计算,从而为实时动态物体三维面形测量技术的工程应用提供较为重要的基础。
在前述理论与技术研究的基础上,研制一套全时间分辨率、全空间分辨率具有任意复杂形状的动态物体三维面形测量系统设备。硬件方面,设计了投影仪与相机的同步时序。针对其中的DMD翻转延时与相机接受到触发信号至感光响应的延时,设计两组测试实验分别测定了延时的大小,实现投影仪与相机精确同步,实验表明该系统能实现每秒220帧的精确同步采集。软件方面,根据前述的关键理论与技术开发出动态物体三维面形测量软件系统,实现了每秒220帧三维面形测量,测量分辨率为1024×768,并且探索了该系统在材料变形、运动追踪、人脸测量和手势与姿态识别中的应用前景。
3D shape measurement technology has been widely used in the aerospace, weapons, automobile and other fields. The shape of key parts of these areas (such as the engine block, blades, exhaust pipes, etc.) is usually very complex, and the3D shape measurement technology not only be able to take full advantage of the existing design to improve the design speed and shorten the development cycle, but also control the product quality and reduce production costs. However, most of the existing3D shape measurement techniques can only measure the static objects. Dynamic3D measurement is increasingly important in many applications such as measuring the deformation of the rotating blades, the aircraft wind tunnel experiment, collision experiments and electromagnetic forming process. The dynamic process of continuous3D shape measurement results can be used to verify the computer simulation results, help to delineate and analyze the variation of the surface morphology and improve manufacture process, so it is very important in theoretical and practical researches.
This paper presents a novel sinusoidal grating projection-based dynamic3D measurement techniques based on the development of nonlinear error correction technique, multi-view phase-shift framework based dynamic3D measurement, high-precision multi-view system calibration and massive fine-grained parallel computing. By accurate synchronization acquisition hardware design and software design, a high accurate, full temporal and spatial resolution, dynamic3D measurement system is developed.
Typically, the phase value accuracy calculated by the three-step phase-shift algorithm will be influenced by the non-linear response of the system. In order to improve phase calculation accuracy, the nonlinear response of the measurement system is modeled. Compared with the existing models, it takes the defocus of projector into consideration and can describe the non-linear distortion of the fringe images in space accurately. On this basis, a gamma parameter calibration algorithm is proposed, and the calibrated gamma value will be encoded into the fringe images before measurement task for phase error compensation. Experimental results showed that the phase error could be control within0.045rad, and the3D measurement error was effectively suppressed, and it laid a good foundation for3D calculation process.
It is very important to achieve full temporal and spatial resolution3D measurement of arbitary shape objects in scientific research and engineering application. So a stereo matching method based on multi-view phase-shift framework is proposed for dynamic3D measurement. In this framework, benefited from the trifocal tensor constraint, each pixel can explore its corresponding point independently only in the wrapped phase-map and reconstruct a3D point in space, so arbitrary shape including the discontinuous surfaces or step-like surfaces can be measured with full spatial resolution. Moreover, as any adjacent three images per camera in the continuous capturing stream can be used to reconstruct one3D frame, the3D acquisition speed can be as fast as the camera capturing speed. So our method can also perform full temporal resolution3D measurements. To enhance the resistance to false correspondence caused by multiple fringe number and phase error, a two-stage strategy, using disparity range constraint and mixed consistency check successively, is proposed to reject the wrong candidates. Further, the accuracy of the corresponding points is refined by performing edge point adjustment and correspondence refinement, and makes the3D result insensitive to the phase error.
System calibration accuracy is very critical to the measurement accuracy, and a multi-view system calibration method is proposed. The calibration procedure contains two steps:(1) calibrating the internal parameters of each camera individually (2) determining the pose of each camera. For single camera calibration problem, Zhang's flexible calibration method is used to obtain the initial values, and these values are optimized by the bundle adjustment strategy to eliminate the influence of the inaccuracy of the target. Then the world coordinate is located on the camera coordinate, the pose of each camera is calibrated by epipolar constraint, and the calibrated translation vector has to be revised to remove the scaling factor. In addition, due to the environmental factors, the pose of the camera might slightly change, which will make the measurement result inaccurate. In order to grarantee the measurement accuracy, a fast and enhanced pose calibration method is used to determining the pose of each camera. Experimental results showed that this method is accurate and stable.
The large amount of data intensive computing is always the bottleneck of real-time dynamic3D measurement. In order to effectively improve the speed of3D calculation, a large-scale, fine-grained parallel computing model, which take full advantage of the performance of GPU parallel computing, is used to design a high-resolution high-precision, real-time dynamic3D parallel computing algorithms in the conventional mainstream chip. The experiment showed that the computation efficiency in the GPU is nearly400times faster than that in the CPU. And it achieved real-time calculation of32000points per millisecond, which will provide an important foundation in real-time3D measurement applications.
On the basis of the above theory and technology researches, a dynamic3D shape measurement equipment is developed. In this equipment, the synchronous timing of the projector with the camera is designed to correctly capture gray scale images. Two test experiments are designed to determine the time delay of DMD flip and time delay of camera receiving a trigger signal and the photosensitive response. The experiments show that the system can achieve precise synchronization acquisition of220per second. On the other hand, a dynamic3D measurement software system is designed to achieve2203D frames per second dynamic measurement with spatial resolution of1024x768. Finally application prospects of this system in material deformation, motion tracking, the face measuring and gestures and gesture recognition is also described.
引文
[1]叶声华,郑继贵.视觉检测技术及应用.中国工程科学,1999,1(1)：49-52
[2]苏显渝.信息光学.北京：科学出版社,1999
[3]Chen F., Brown G, Song M.. Overview of three-dimensional shape measurement using optical methods. Optical Engineering,2000,39(1):10-22
[4]Blais F. Review of 20 years of range sensor development. Journal of Electronic Imaging,2004,13(1):231-240
[5]Jason Geng. DLP-Based Stuctured Light 3D Imaging Technologies and Applications. Proceedings of IEEE Conference on Intelligent Transportation Systems Society,2011.
[6]Giovanna Sansoni, Marco Trebeschi and Franco Docchio. State-of-The-Art and applications of 3D imaging sensors in industry, Cultural Heritage, Medicine, and criminal investigation. Sensors,2009,9(1):568-601
[7]潘伟,反向工程中光栅投影测量系统关键技术的研究：[博士学位论文].上海：上海交通大学,2004
[8]Yuan Li, Youfu Li, Qinglin Wang. Measurement and defect detection of the weld bead based on online vision inspection. IEEE Transactions on instrumentation and measurement,201059(7):1841-1847
[9]Francisco Javier Brosed, Juan Jose Aguilar, David Guillomia.3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot. Sensors,2011,11(1):90-110
[10]Oystein Skotheim, Jens Olav Nygaard, Jens Thielemann, "A flexible 3D vision system based on structured light for inline product inspection," Proc. SPIE 6805,2008
[11]Deokhwa Hong, Hyunki Lee, Min Young Kim. Sensor fusion of phase measuring profilometry and stereo vision for three-dimensional inpection of electronic compoinents assembled on printed circuit boards. Applied Optics,2009,48(21): 4158-4169
[12]梁晋,肖振中,唐正宗,刘建伟.大型飞机风洞变形测量的相机标定研究.西安交通大学学报,2009,43(3)：55-59
[13]张阳,臧顺来,郭翔,梁晋,郭成.基于数字散斑应变测量法的薄板各向异性力学性能研究.材料工程,2012,18(4)：46-11
[14]J. I. Laughner, S. Zhang, H. Li, and I. R. Efimov, Mapping cardiac surface mechanics with structured light imaging. American Journal of Physiology:Heart and Circular Physiology,2012,303(6):712-720
[15]Asundi AK, Sajan MR. Digital drum camera for dynamic reording. Optcial Engineering,1996,35(6):1707-1713
[16]http://web.mit.edu/museum/exhibitions/edgertonexhibit/flashes.html
[17]Sajan M.R., TayC.J., ShangH.M., Asundi A. TDI imaging - a tool for Profilometry and automated visual inspection. Optics and Laser in Engineering,1998,29(6): 403-411
[18]Asundi A., Sajan M. R. Dynamic photoelasticity using LED and Polaroid film. Applied Optics,1995,34(34):8064-8065
[19]Tollier L, Fabbro R. Study on the laser driven spallation process by the CISAR interferemetry technique. J. Applied Physics,1998,83(4):1231-1237
[20]陈光华,李泽仁.激光干涉测速技术在爆轰实验中的应用.爆轰波与冲击波,1999,(2)：79-84
[21]李泽仁,姚建锉.VISAR测速中的信号丢失及丢失条纹数的确定.爆炸与冲击,1999,19(2)：182-187
[22]Yamaguehi L.. Hologram, spechle and computer. Optics and lasers in engineering. 2003,39(4):23-34
[23]Zhang S. Recent progresses on real-time 3-D shape measurement using digital fringe projection techniques, Opt. Laser Eng.,2010,48(2):149-158
[24]Adrian A. Dorringtion, Cameron D.B. Kelly, Shane H. McClure et al. Advantages of 3D time of flight range imaging cameras in machine vision applications. The 16th Electronics New Zealand Conference, Dunedin, New Zealand,2009,95-99
[25]Ganapathi, V., Plagemann, C., Koller, D. et al. Real time motion capture using a single time of flight camera. IEEE Conference on Computer Vision and Pattern Recognition, 2010,755-762
[26]Bogumil Bartczak, Reinhard Koch. Dense depth maps from low resolution time of flight depth and high resolution color views. Proceedings of the 5th International Symposium on Advances in Visual Computing Part Ⅱ,2009,228-239
[27]Hsu S, Acharya S, Rafii A, New R. Performance of a time-of-flight range camera for intelligent vehicle safety applications. Advanced Microsystems for Automotive Applications,2006,19:205-219
[28]沐光雨.基于时间飞行深度传感器对三维物体的建模应用研究：[博士学位论文].吉林：吉林大学,2011
[29]http://en.wikipedia.org/wiki/ZCam
[30]Uwe Hahne, Marc Alexa. Depth imaging by combining time of flight and on demand stereo. Dynamic 3D Imaging Lecture Notes in Computer Science,2009,5742:70-83
[31]Schuon S. Theobalt C, Davis J. et al. High-quality scanning using time of flight depth superresolution. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop,2008,23-28
[32]Yan Cui, Sebastian Schuon, Sebastian Thrun et al. Algorithm for 3D shape scanning with a depth camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(5):1039-1049
[33]S.K. Nayar, M. Watanabe and M. Noguchi. Real-time focus range sensor, IEEE International Conference on Computer Vision,1995,995-1001
[34]S.K. Nayar, M. Watanabe and M. Noguchi. Real-time focus range sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(12):1186-1198
[35]M.Watanabe, S.K. Nayer. Rational filter for passive depth from defocus. International Journal on Computer Vision,1998,27(3):203-225
[36]Robert J. Woodham. Photometric method for determining surface orientation from multiple images. Optical Engineering,1980,19(1):139-144
[37]Diego Nehab, Szymon Rusinkiewicz, James Davis et al. Efficiently combining positions and normal for precise 3D geometry. Proceedings of ACM SIGGRAPH Conference and Exhibition,2005,24(3):536-543
[38]Daniel Vlasic, Pieter Peers, Ilya Baran. Dynamic Shape Capture using Multi-View Photometric Stereo, Proceedings of the ACM SIGGRAPH Conference and Exhibition, 2009,28(5):1-11
[39]Luhman, T, Robson S., Kyle S. et al. Close range photogrammetry:principles, techniques and applications. New York:Wiley & Sons.,2006
[40]Davis J, Ramamoorthi R, Rusinkiewicz S. Spacetime stereo:a unifying framework for depth from triangulation, IEEE Trans Pattern Anal. Mach. Intell,2005,27(2):1-7
[41]J.J. Aguilar, F. Torres, M.A. Lope. Stereo vision for 3D measurement:accuracy analysis, calibration and industrial applications. Measurement,1996,18(4):193-200
[42]张德海,梁晋,唐正宗等.基于近景摄影测量和三维光学测量的大幅面测量新方法.中国机械工程,2009,20(7)：817-822
[43]肖振中.基于工业摄影和机器视觉的三维形貌与变形测量关键技术研究：[博士学位论文].西安,西安交通大学,2010
[44]W. H. Peters, W. F. Ranson. Digital imaging techniques in experimental stress analysis. Opt. Eng.,1982,21(3):427-431
[45]I. Yamaguchi. Automatic Measurement of In-plane Translation by Speckle Correlation Using a Linear Image Sensor. Journal of Phys. E.,1986,19(11):944-946
[46]戴宜全,孙泽阳,吴刚,何小元.基于数字图像相关法的混凝土全场变形测量,东南大学学报,2010,40(4)：829-834
[47]顾国庆,王开福.物体变形测量的相移数字散斑剪切干涉术,激光杂志,2011,32(4)：16-17
[48]孟利波,金观昌,姚学锋,罗敏.数字散斑相关方法及其在碳纤维复合材料压力容器变形测量中的应用,光学技术,2006,32(2)：163-168
[49]刘小勇.数字图像相关方法及其在材料力学性能测试中的应用：[博士学位论文].吉林：吉林大学,2012
[50]潘兵,吴大方,郭保桥.数字体图像相关方法中基于迭代最小二乘法的物体内部变形测量,实验力学,2011,26(6)：665-673
[51]曾丽梅,曹益平.采用多特征窗口识别的散斑位移测量新方法,中国激光,2009,36(4)：949-953
[52]王志勇,王磊,郭伟,沈珉.数字图像相关方法最优散斑尺寸,天津大学学报,2010,43(8)：674-678
[53]王希军.激光散斑的亚像素位移法计算及比较.中国光学,2012,5(6)：652-657
[54]Hongjian Shi, Hongwei Ji, GuobiaoYang, Xiaoyuan He. Shape and deformation measurement system by combining fringe projection and digital image correlation, Optics& Laser in Engineering,2013,51(1):47-53
[55]http://www.gom.com/metrology-systems/system-overview/aramis.html
[56]http://www.correlatedsolutions.com/index.php/products/vic-3d
[57]孟利波.数字散斑相关方法的研究和应用：[博士学位论文].北京：清华大学,2005
[58]唐正宗,梁晋,肖振中等.用于三维变形测量的数字图像相关系统.光学精密工程,2010,18(10)：2244-2253
[59]曾祥福,刘程林,马少鹏.高速三维数字图像相关系统及其动载三维变形测量,北京理工大学学报,2012,32(4)：364-369
[60]杨福俊,房亮,何小元.基于单幅数字散斑投影及图像相关的离面振动测量.光学技术,2007,32(3)：323-326
[61]李美菊,苏显渝.投影数字散斑的立体匹配,2005,28(4)：550-553
[62]Gorthi S, Rastogi P. Fringe projection techniques:whither we are?. Opt. Laser Eng., 2010,48(2):133-140
[63]Jason Geng. DLP-based structured light 3D imaging technologies and applications. Proc. of SPDE Vol.7932,79320B-1,2011
[64]C. L. Heike, K. Upson, E. Stuhaug et al.3D digital stereo photogrammetry:a practical guide to facial image acquisition. Head Face Med.2010,6(1):18-25
[65]J. Geng. Method and apparatus for 3D imaging using light pattern having multiple sub-patterns. U.S. patent 6,700,669,2004
[66]Forster F. A High-Resolution and High Accuracy Real-Time 3D Sensor Based on Structured Light, IEEE The Third International Symposium on 3D Data Processing, Visualization and Transmission,2006
[67]Kawasaki H, Furukawa R, Sagawa R, et al. Dynamic scene shape reconstruction using a single structured light pattern. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2008, Vol.1-12:2806-2813
[68]韦争亮,钟约先,袁朝龙.基于彩色栅线的结构光动态三维测量技术研究.光学技术,2009,35(4)：569-573
[69]S. Fernandez, J. Salvi, T. Pribanic. Absolute phase mapping for one-shot dense pattern projection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) San Francisco, Calif,2010
[70]N. G. Durdle, J. Thayyoor, and V. J. Raso. An improved structured light technique for surface reconstruction of the human trunk. In IEEE Canadian Conference on Electrical and Computer Engineering,1998,2:874-877
[71]Chen SY, Li YF, Zhang JW. Vision Processing for Real-time 3-D Data Acquisition Based on Coded Structured Light. IEEE Transactions on Image Processing,2008, 17(2):167-176
[72]Osman Ulusoy, F. Calakli, and G Taubin. Robust one-shot 3D scanning using loopy belief propagation, in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (IEEE Computer Society,2010,15-22
[73]P. Payeur and D. Desjardins. Structured light stereoscopic imaging with dynamic pseudo-random patterns, in Image Analysis and Recognition, Lecture Notes in Computer Science,2009, Vol.5627
[74]Osman Ulusoy, F. Calakli, and G. Taubin, "One-shot scanning using DeBruijn spaced grids," in 2009 IEEE 12th International Conference on Computer Vision Workshops (ICC V Workshops),2009,1786-1792
[75]P. M. Grin, L. S. Narasimhan, and S. R. Yee. Generation of uniquely encoded light patterns for range data acquisition. Pattern Recog.,1992,25(6):609-616
[76]http://www.microsoft.com/zh-cn/kinectforwindows/
[77]Mitsuo Takeda, Hideki Ina, Sejii Kobayashi. Fourier-transform method of fringe-pattern analysis of computer-based topography and interferometry. J. Opt. Soc. Am.,1982,72(1):156-160
[78]Mitsuo Takeda, Kazuhiro Mutoh. Fourier-transform profilometry for the auto measurement of 3D object shapes. Applied Optics,1983,22(24):3977-3982
[79]Su X., Chen W. Fourier transform profilometry:a review. Optics and Lasers in Engineering,2001,35(5):263-284
[80]张启灿.动态过程三维面形测量技术研究：[博士学位论文].成都：四川大学,2005
[81]Guo H., Huang P.3D shape measurement by use of a modified Fourier-transform method. In:Proceedings SPIE Vol.7066,2008
[82]Ghiglia D., Pritt M.. Two-dimensional phase unwrapping:theory, algorithms, and software. New York:Wiley,1998
[83]Su X., Chen W.. Reliability-guided phase unwrapping algorithm:a review. Optics and Lasers in Engineering,2004,42(3):245-261
[84]Su X.Y., Zhang Q.C.. Dynamic 3-d shape measurement method:A review. Opt. Laser. Eng.,2010,48(2):191-204
[85]C. Guan, L. G. Hassebrook, D. L. Lau. Composite structured light pattern for three-dimensional video. Optics Express,2003,11(5):406-417
[86]HJ. Chen, J. Zhang, D.J. Lv et al.3D shape measurement by composite pattern projection and hybrid processing. Optics Express,2007,15(19):12318-12330
[87]Jing Xu, Bingtuan Gao, Jinhua Han et al. Real-time 3D profile measurement by using the composite pattern based on the binary pattern. Optics & Laser Technology,2012, 44(3):587-593
[88]Hall-Holt O., Rusinkiewicz S.. Stripe boundary codes for real-time structured-light range scanning of moving objects. The 8th IEEE International Conference on Computer Vision,2001,359-366
[89]Rusinkiewicz S, Hall-Holt O, Levoy M. Real-time 3d model acquisition. ACM Trans. Graph.,2002,21(3):438-446
[90]Zhang L., Snavely N., Curless B., et al. Space-time Faces:High Resolution Capture for Modeling and Animation. ACM Transactions on Graphics,2004,23(3):548-558
[91]S Zhang, PS Huang. Novel method for structured light system calibration. Opt. Eng., 2006,45(8),0.83601
[92]S Zhang, X Li and S-T Yau. Multilevel quality-guided phase unwrapping algorithm for real-time 3-D shape reconstruction. Appl. Opt,2007,46(1),50-57
[93]S Zhang, PS Huang. Phase error compensation for a 3-D shape measurement system based on the phase shifting method. Opt. Eng.,2007,46(6):063601
[94]S Zhang, S-T Yau. Generic nonsinusoidal phase error correction for three-dimensional shape measurement using a digital video projector. Appl. Opt.,2007,46(1):36-43
[95]S Zhang, D Royer, S-T Yau. GPU-assisted high-resolution, real-time 3-D shape measurement. Opt. Express,2006,14(20):9120-9129
[96]Zhang S. High-resolution, real-time 3D shape measurement:[PhD Dissertation]. NewYork:Stony Brook University,2006
[97]Zhang S, Yau ST. High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method. Opt. Express.,2006,14(7):2644-2649
[98]S. Lei, S. Zhang. Digital sinusoidal fringe generation:defocusing binary patterns vs. focusing sinusoidal patterns. Opt. Laser Eng.,2010,48(5):561-569
[99]S. Zhang. Flexible 3-D shape measurement using projector defocusing:extended measurement range. Opt. Lett.,2010,35(7):931-933
[100]S. Zhang, D. van der Weide, J. Oliver. Superfast phase-shifting method for 3-D shape measurement. Opt. Express,2010,18(9):9684-9689
[101]Y. Gong, S. Zhang. Ultrafast 3-D shape measurement with an off-the-shelf DLP projector. Opt. Express,2010,18(19):19743-19754
[102]Y. Wang, S. Zhang. Optimal pulse width modulation for sinusoidal fringe generation with projector defocusing. Opt. Lett.,2010,35(24):4121-4123
[103]W. Lohry, S. Zhang.3D shape measurement with 2D area modulated binary patterns. Opt. Laser Eng.2012,50(7),917-921
[104]W. Lohry, S. Zhang. Genetic method to optimize binary dithering technique for high-quality fringe generation. Opt. Lett.,2013,38(4):540-542
[105]L. Merner, Y. Wang, S. Zhang. Accurate calibration for 3D shape measurement system using a binary defocusing technique. Opt. Laser Eng.,2013,51(5),514-519
[106]Yajun Wang, S. Zhang, James H. Oliver.3D shape measurement technique for multiple rapidly moving objects. Optics Express,2011,19(9):8539-8545
[107]Yajun Wang, S. Zhang. Superfast multi-frequency phase-shifting technique with optimal pulse with modulation. Optics Express,2011,19(6):5149-5155
[108]S. Zhang, Composite phase-shifting algorithm for absolute phase measurement. Opt. Laser Eng.,2012,50(11):1538-1541
[109]Yajun Wang, S. Zhang. Novel phase-coding method for absolute phase retrieval. Optics Letters,2012,37(11):2067-2069
[110]Kai Liu, Yongchang Wang, Daniel L. Lau, Qi Hao, Laurence G. Hassebrook. Dual-frequency pattern scheme for high-speed 3D shape measurement. Optics Express,2010,18(5):5229-5244
[111]Huntley J., Saldner H.. Shape measurement by temporal phase unwrapping: comparision of unwrapping algorithms. Measurement Science and Technology,1997, 8(9):986-992
[112]Saldner H., Huntley J. Temporal phase unwrapping:application to surface profiling of discontinuous objects. Applied optics,1997,36(13):2770-2775
[113]Huntley J., Coggrave C. Progress in phase unwrapping, Proc. SPIE 3407, International Conference on Applied Optical Metrology,1998
[114]Weise T, Leibe B, Van Gool L. Fast 3D Scanning with Automatic Motion Compensation. IEEE Conference on Computer Vision and Pattern Recognition,2007, 1(8):2462-2469
[115]Weise T, Li H, Van Gool L. Face/off:Live facial puppetry. Proceedings of the ACM SIGGRAPH,2009
[116]Yong Li, Cuifang Zhao, Yixian Qian et al. High-speed and dense three-dimensional surface acquisition using defocused binary patterns for spatially isolated objects. Optics Express,2010,18(21):21635-21642
[117]Chao Zuo, Qian Chen, Guohua Gu et al. High-speed three-dimensional profilometry for multiple objects with complex shapes. Optics Express,2012,20(17):19493-19511
[118]李中伟,王从军,史玉升.3D测量系统中的高精度摄像机标定算法.光电工程,2008,35(4)：58-63
[119]Zhongwei Li, Yusheng Shi, Congjun Wang and Yuanyuan Wang. Accurate calibration method for a structured light system. Optical Engineering,2008,47(5):053604
[120]李中伟,王从军,史玉升等.结构光测量中的高精度相位误差补偿算法,光学学报,2008,28(8)：1527-1532
[121]Zhongwei Li, Yusheng Shi, Congjun Wang, Dahui Qin, and Kui Huang. Complex Object 3D Measurement Based on Phase-Shifting and a Neural Network. Optics Communications,2009,282(14):2699-2706
[122]李中伟.基于数字光栅投影的结构光三维测量技术与系统研究：[博士学位论文].武汉：华中科技大学,2009
[123]Bing Pan, Qian Kemao, LeiHuang. Phase error analysis and compensation for nonsinusoidal waveforms in phase-shifting digital fringe projection profilometry. Optics Letter,2009,34(2):416-418
[124]Guo H., He H., Chen M.. Gamma correction for digital fringe projection profilometry. Applied Optics,2004,43(14):2906-2914
[125]M. J. Baker, J. Xi, and J. F. Chicharo. Elimination of non-linear luminance effects for digital video projection phase measuring profilometers. In 4th IEEE International Symposium on Electronic Design, Test and Applications,2008,496-501
[126]Chen X., Xi J., Jin Y. Phase error compensation method using smoothing spline approximation for a three-dimensional shape measurement system based on gray-code and phase-shift light projection. Optical Engineering,2008,47:113601
[127]Kai Liu, Yongchang Wang, Daniel L. Lau. Gamma model and its analysis for phase measuring profilometry. J. Opt. Soc. Am. A,2010,27(3):553-562
[128]Thang Hoang, Bin Pan, Dung Nguye et al. Generic gamma correction for accuracy enhacement in fringe-projection profilometry. Optics Letter,2010,35(12):1992-1994
[129]C.R. Coggrave, and J. M. Huntley. Optimization of a shape measurement system based on spatial light modulators. Opt. Eng.,2000,39(1):91-98
[130]S. Lei, S. Zhang. Flexible 3-D Shape Measurement Using Projector Defocusing. Opt. Lett.,2009,34(20):3080-3082
[131]Ke Chen, Jiangtao Xi, Yanguang Yu et al. A composite quality-guided phase unwrapping algorithm for fast 3D profile measurement, Proc. SPIE Conference, Optics & Photonics,8563,2012
[132]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision Cambridge University Press, Cambridge,2000
[133]Heikkia J., Silven O.. A four-step camera calibration procedure with implicit image correction. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997
[134]Rui Ishiyama, Shizuo Sakamoto, Johji Tajima. Absolute phase measurements using geometric constrains between multiple camreas and projectors. Appl. Opt.,2007, 46(17):3528-3538
[135]Christian Brauer-Burchardt, Christoph Munkelt, Matthias Heinze, Using geometric constrains to solve the point correspondecce problem in fringe projection based 3D measuring systems, Proc. ICIAP,2011,265-274
[136]R. Hartley, R. Gutpa, T. Chang. Stereo from uncalibrated cameras. IEEE International Conference on Computer Vision and Pattern Recognition,1992
[137]Richard I. Hartley. Theory and practice of projective rectification. International Journal of Computer Vision,1999,35(2):115-127
[138]Towers C., Towers D., Jones J.. Absolute fringe order calculation using optimized multi-frequency selection in full-field profilometry. Optics and Lasers in Engineering, 2005,43(7):788-800
[139]Faig W.. Calibration of close-range photogrammetry systems:Mathematical formulation. Photogrammetric engineering and remote sensing,1975,41(12): 1479.1486
[140]Tsai R.. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of robotics and automation,1987,3(4):323-344
[141]Zhang Z.. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334
[142]Zhang Z.. Flexible camera calibration by viewing a plane from unknown orientations. The Proceedings of the 7th IEEE International Conference on Computer Vision,1999, 1:666-673
[143]http://www.vision.caltech.edu/bouguetj/calib_doc/
[144]Zhang Z.. Camera calibration with one-dimensional objects. IEEE Transaction on pattern analysis and machine intelligence,2004,26(7):893-899
[145]Faugeras O., Luong Q., Maybank S.. Camera self-calibration:theory and experiments. Springer,1992
[146]Hartley R.. Self-calibration of stationary cameras. International Journal of Computer Vision,1997,22(1):5-23
[147]Tomas Svoboda, Daniel Martinec, and Tomas Pajdla. A convenient multi-camera self-calibration for virtual environments. PRESENCE:Teleoperators and Virtual Environments,2005,14(4):407-422
[148]Yasutaka Furukawa, Jean Ponce. Accurate camera calibration from multi-view stereo and bundle adjustment. Int. J. Comput. Vis.,2009,84(3):257-268
[149]陈厚道.面向快速成形的反求工程关键技术的研究：[硕士学位论文].武汉：华中科技大学,2003
[150]谢永辉.三维激光扫描系统的标定自动化技术与精度研究：[硕士学位论文].武汉：华中科技大学,2003
[151]江舜妹.柔性测量臂激光测量头标定及精度研究：[硕士学位论文].武汉：华中科技大学,2007
[152]杨彪.人工神经网络在反求中的应用研究：[硕士学位论文].武汉：华中科技大学,2008
[153]黄奎.多关节式坐标测量系统的关键技术研究：[博士学位论文].武汉：华中科技大学,2010
[154]Brown D. C. The bundle adjustment-progress and prospects. International Archives of Photogrammetry, ISP Congress, Helsinki,1976,21(3):1-33
[155]Bill Triggs, Philip F. McLauchlan, Richard I. Hartley. Bundle adjustment a modern synthesis. Lecture Notes in Computer Science,2000,1883:298-372
[156]V. Lepetit, F. Moreno-Noguer, P. Fua. EPnP:An accurate O(n) solution to the PnP Problem. International Journal of Computer Vision,2009,81(2):155-166
[157]Long Quan and Zhangdan Lan. Linear N-point camera pose determination. IEEE transactions on Pattern Analysis and Machine Intelligence,1999,21(8):774-780
[158]Chien-Ping Lu, Gregory D. Hager. Fast and globally convergent pose estimation from video images. IEEE transactions on Pattern Analysis and Machine Intelligence,2000, 22(6):610-622
[159]Denis Oberkampf, Daniel F. Dementhon, Larry S. Davis. Iterative pose estimation using coplanar feature points. Computer Vision and Image Understanding,1996, 63(3):495-511
[160]Gerald Schweighofer, Axel Pinz. Robust pose estimation from a planar target. IEEE transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2024-2030
[161]D.W. Eggert, A. Lorusso, R.B. Fisher. Estimating 3D rigid body transformations:a comparison of four major algorithms. Machine Vision and Applications,1997,9(5-6): 272-290
[162]张舒,诸艳利,赵开勇等.GPU高性能运算之CUDA.北京：中国水利水电出版社,2009
[163]Stivala A., Stuckey P., Wirth A. Fast and accurate protein substructure searching with simulated annealing and GPUs. BMC bioinformatics,2010,11(1):446-462
[164]Ufimtsev I.S. Marinez T.J.. Quantum chemistry on graphical processing processing units.1. strategies for two-electron integral evaluation. Journal of Chemical Theory and Computation,2008,4(2):222-231
[165]Stone J.E., Hardy D.J., Ufimtsev I.S. et al. GPU-accelerated molecular modeling coming of age. Journal of Molecular Graphics and Modeling,2010,29(2):116-125
[166]Catanzaro B., Sundaram N., Keutzer K.. Fast support vector machine training and classification on graphics processors. Proceedings of the 25th International Conference on Machine Learning, New York:ACM,2008
[167]Mamoru Miura, Kinya Fudano, Koichi Ito et al. GPU implementation of phase-based stereo correspondence and its application.19th IEEE International Conference on Image Processing,2012,1697-1700
[168]Chun-qin Shi, Liyan Zhang. A 3D shape measurement system based on random pattern projection. Fifth International Conference on Frontier of Computer Science and Technology,2010
[169]Zheng Zhang, Hock Soon Seah. CUDA acceleration of 3D dynamic scene reconstruction and 3D motion estimation for motion capture. IEEE 18th International Conference on Parallel and Distributed System,2012
[170]余进.孔周高温弹塑性疲劳变形的检测研究：[硕士学位论文].合肥：中国科学技术大学,2009
[171]于纪言.运动弹丸追踪摄影方法及管件技术研究：[博士学位论文].南京：南京理工大学,2007
[172]刘伟锋.人脸表情识别研究：[博士学位论文].合肥：中国科学技术大学,2007
[173]Amberg B., Romdhani S., Vetter T. Optimal step non-rigid ICP algorithms for surface registration. In Proceedings of IEEE Computer Vision Pattern Recognition,2007
[174]Wang M., Jenke P., Huang Q., Bokeloh M., Guibas L., Schilling A. Reconstruction of deforming geometry from time-varying point clouds. In Symposium on Geometry processing,2007,49-58
[175]Brown B. J., Rusinkiewicz S.:Global non-rigid alignment of 3D scans. ACM Trans. Graph.,2007,26(3):1-10
[2]苏显渝.信息光学.北京：科学出版社,1999
[3]Chen F., Brown G, Song M.. Overview of three-dimensional shape measurement using optical methods. Optical Engineering,2000,39(1):10-22
[4]Blais F. Review of 20 years of range sensor development. Journal of Electronic Imaging,2004,13(1):231-240
[5]Jason Geng. DLP-Based Stuctured Light 3D Imaging Technologies and Applications. Proceedings of IEEE Conference on Intelligent Transportation Systems Society,2011.
[6]Giovanna Sansoni, Marco Trebeschi and Franco Docchio. State-of-The-Art and applications of 3D imaging sensors in industry, Cultural Heritage, Medicine, and criminal investigation. Sensors,2009,9(1):568-601
[7]潘伟,反向工程中光栅投影测量系统关键技术的研究：[博士学位论文].上海：上海交通大学,2004
[8]Yuan Li, Youfu Li, Qinglin Wang. Measurement and defect detection of the weld bead based on online vision inspection. IEEE Transactions on instrumentation and measurement,201059(7):1841-1847
[9]Francisco Javier Brosed, Juan Jose Aguilar, David Guillomia.3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot. Sensors,2011,11(1):90-110
[10]Oystein Skotheim, Jens Olav Nygaard, Jens Thielemann, "A flexible 3D vision system based on structured light for inline product inspection," Proc. SPIE 6805,2008
[11]Deokhwa Hong, Hyunki Lee, Min Young Kim. Sensor fusion of phase measuring profilometry and stereo vision for three-dimensional inpection of electronic compoinents assembled on printed circuit boards. Applied Optics,2009,48(21): 4158-4169
[12]梁晋,肖振中,唐正宗,刘建伟.大型飞机风洞变形测量的相机标定研究.西安交通大学学报,2009,43(3)：55-59
[13]张阳,臧顺来,郭翔,梁晋,郭成.基于数字散斑应变测量法的薄板各向异性力学性能研究.材料工程,2012,18(4)：46-11
[14]J. I. Laughner, S. Zhang, H. Li, and I. R. Efimov, Mapping cardiac surface mechanics with structured light imaging. American Journal of Physiology:Heart and Circular Physiology,2012,303(6):712-720
[15]Asundi AK, Sajan MR. Digital drum camera for dynamic reording. Optcial Engineering,1996,35(6):1707-1713
[16]http://web.mit.edu/museum/exhibitions/edgertonexhibit/flashes.html
[17]Sajan M.R., TayC.J., ShangH.M., Asundi A. TDI imaging - a tool for Profilometry and automated visual inspection. Optics and Laser in Engineering,1998,29(6): 403-411
[18]Asundi A., Sajan M. R. Dynamic photoelasticity using LED and Polaroid film. Applied Optics,1995,34(34):8064-8065
[19]Tollier L, Fabbro R. Study on the laser driven spallation process by the CISAR interferemetry technique. J. Applied Physics,1998,83(4):1231-1237
[20]陈光华,李泽仁.激光干涉测速技术在爆轰实验中的应用.爆轰波与冲击波,1999,(2)：79-84
[21]李泽仁,姚建锉.VISAR测速中的信号丢失及丢失条纹数的确定.爆炸与冲击,1999,19(2)：182-187
[22]Yamaguehi L.. Hologram, spechle and computer. Optics and lasers in engineering. 2003,39(4):23-34
[23]Zhang S. Recent progresses on real-time 3-D shape measurement using digital fringe projection techniques, Opt. Laser Eng.,2010,48(2):149-158
[24]Adrian A. Dorringtion, Cameron D.B. Kelly, Shane H. McClure et al. Advantages of 3D time of flight range imaging cameras in machine vision applications. The 16th Electronics New Zealand Conference, Dunedin, New Zealand,2009,95-99
[25]Ganapathi, V., Plagemann, C., Koller, D. et al. Real time motion capture using a single time of flight camera. IEEE Conference on Computer Vision and Pattern Recognition, 2010,755-762
[26]Bogumil Bartczak, Reinhard Koch. Dense depth maps from low resolution time of flight depth and high resolution color views. Proceedings of the 5th International Symposium on Advances in Visual Computing Part Ⅱ,2009,228-239
[27]Hsu S, Acharya S, Rafii A, New R. Performance of a time-of-flight range camera for intelligent vehicle safety applications. Advanced Microsystems for Automotive Applications,2006,19:205-219
[28]沐光雨.基于时间飞行深度传感器对三维物体的建模应用研究：[博士学位论文].吉林：吉林大学,2011
[29]http://en.wikipedia.org/wiki/ZCam
[30]Uwe Hahne, Marc Alexa. Depth imaging by combining time of flight and on demand stereo. Dynamic 3D Imaging Lecture Notes in Computer Science,2009,5742:70-83
[31]Schuon S. Theobalt C, Davis J. et al. High-quality scanning using time of flight depth superresolution. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop,2008,23-28
[32]Yan Cui, Sebastian Schuon, Sebastian Thrun et al. Algorithm for 3D shape scanning with a depth camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(5):1039-1049
[33]S.K. Nayar, M. Watanabe and M. Noguchi. Real-time focus range sensor, IEEE International Conference on Computer Vision,1995,995-1001
[34]S.K. Nayar, M. Watanabe and M. Noguchi. Real-time focus range sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(12):1186-1198
[35]M.Watanabe, S.K. Nayer. Rational filter for passive depth from defocus. International Journal on Computer Vision,1998,27(3):203-225
[36]Robert J. Woodham. Photometric method for determining surface orientation from multiple images. Optical Engineering,1980,19(1):139-144
[37]Diego Nehab, Szymon Rusinkiewicz, James Davis et al. Efficiently combining positions and normal for precise 3D geometry. Proceedings of ACM SIGGRAPH Conference and Exhibition,2005,24(3):536-543
[38]Daniel Vlasic, Pieter Peers, Ilya Baran. Dynamic Shape Capture using Multi-View Photometric Stereo, Proceedings of the ACM SIGGRAPH Conference and Exhibition, 2009,28(5):1-11
[39]Luhman, T, Robson S., Kyle S. et al. Close range photogrammetry:principles, techniques and applications. New York:Wiley & Sons.,2006
[40]Davis J, Ramamoorthi R, Rusinkiewicz S. Spacetime stereo:a unifying framework for depth from triangulation, IEEE Trans Pattern Anal. Mach. Intell,2005,27(2):1-7
[41]J.J. Aguilar, F. Torres, M.A. Lope. Stereo vision for 3D measurement:accuracy analysis, calibration and industrial applications. Measurement,1996,18(4):193-200
[42]张德海,梁晋,唐正宗等.基于近景摄影测量和三维光学测量的大幅面测量新方法.中国机械工程,2009,20(7)：817-822
[43]肖振中.基于工业摄影和机器视觉的三维形貌与变形测量关键技术研究：[博士学位论文].西安,西安交通大学,2010
[44]W. H. Peters, W. F. Ranson. Digital imaging techniques in experimental stress analysis. Opt. Eng.,1982,21(3):427-431
[45]I. Yamaguchi. Automatic Measurement of In-plane Translation by Speckle Correlation Using a Linear Image Sensor. Journal of Phys. E.,1986,19(11):944-946
[46]戴宜全,孙泽阳,吴刚,何小元.基于数字图像相关法的混凝土全场变形测量,东南大学学报,2010,40(4)：829-834
[47]顾国庆,王开福.物体变形测量的相移数字散斑剪切干涉术,激光杂志,2011,32(4)：16-17
[48]孟利波,金观昌,姚学锋,罗敏.数字散斑相关方法及其在碳纤维复合材料压力容器变形测量中的应用,光学技术,2006,32(2)：163-168
[49]刘小勇.数字图像相关方法及其在材料力学性能测试中的应用：[博士学位论文].吉林：吉林大学,2012
[50]潘兵,吴大方,郭保桥.数字体图像相关方法中基于迭代最小二乘法的物体内部变形测量,实验力学,2011,26(6)：665-673
[51]曾丽梅,曹益平.采用多特征窗口识别的散斑位移测量新方法,中国激光,2009,36(4)：949-953
[52]王志勇,王磊,郭伟,沈珉.数字图像相关方法最优散斑尺寸,天津大学学报,2010,43(8)：674-678
[53]王希军.激光散斑的亚像素位移法计算及比较.中国光学,2012,5(6)：652-657
[54]Hongjian Shi, Hongwei Ji, GuobiaoYang, Xiaoyuan He. Shape and deformation measurement system by combining fringe projection and digital image correlation, Optics& Laser in Engineering,2013,51(1):47-53
[55]http://www.gom.com/metrology-systems/system-overview/aramis.html
[56]http://www.correlatedsolutions.com/index.php/products/vic-3d
[57]孟利波.数字散斑相关方法的研究和应用：[博士学位论文].北京：清华大学,2005
[58]唐正宗,梁晋,肖振中等.用于三维变形测量的数字图像相关系统.光学精密工程,2010,18(10)：2244-2253
[59]曾祥福,刘程林,马少鹏.高速三维数字图像相关系统及其动载三维变形测量,北京理工大学学报,2012,32(4)：364-369
[60]杨福俊,房亮,何小元.基于单幅数字散斑投影及图像相关的离面振动测量.光学技术,2007,32(3)：323-326
[61]李美菊,苏显渝.投影数字散斑的立体匹配,2005,28(4)：550-553
[62]Gorthi S, Rastogi P. Fringe projection techniques:whither we are?. Opt. Laser Eng., 2010,48(2):133-140
[63]Jason Geng. DLP-based structured light 3D imaging technologies and applications. Proc. of SPDE Vol.7932,79320B-1,2011
[64]C. L. Heike, K. Upson, E. Stuhaug et al.3D digital stereo photogrammetry:a practical guide to facial image acquisition. Head Face Med.2010,6(1):18-25
[65]J. Geng. Method and apparatus for 3D imaging using light pattern having multiple sub-patterns. U.S. patent 6,700,669,2004
[66]Forster F. A High-Resolution and High Accuracy Real-Time 3D Sensor Based on Structured Light, IEEE The Third International Symposium on 3D Data Processing, Visualization and Transmission,2006
[67]Kawasaki H, Furukawa R, Sagawa R, et al. Dynamic scene shape reconstruction using a single structured light pattern. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2008, Vol.1-12:2806-2813
[68]韦争亮,钟约先,袁朝龙.基于彩色栅线的结构光动态三维测量技术研究.光学技术,2009,35(4)：569-573
[69]S. Fernandez, J. Salvi, T. Pribanic. Absolute phase mapping for one-shot dense pattern projection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) San Francisco, Calif,2010
[70]N. G. Durdle, J. Thayyoor, and V. J. Raso. An improved structured light technique for surface reconstruction of the human trunk. In IEEE Canadian Conference on Electrical and Computer Engineering,1998,2:874-877
[71]Chen SY, Li YF, Zhang JW. Vision Processing for Real-time 3-D Data Acquisition Based on Coded Structured Light. IEEE Transactions on Image Processing,2008, 17(2):167-176
[72]Osman Ulusoy, F. Calakli, and G Taubin. Robust one-shot 3D scanning using loopy belief propagation, in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (IEEE Computer Society,2010,15-22
[73]P. Payeur and D. Desjardins. Structured light stereoscopic imaging with dynamic pseudo-random patterns, in Image Analysis and Recognition, Lecture Notes in Computer Science,2009, Vol.5627
[74]Osman Ulusoy, F. Calakli, and G. Taubin, "One-shot scanning using DeBruijn spaced grids," in 2009 IEEE 12th International Conference on Computer Vision Workshops (ICC V Workshops),2009,1786-1792
[75]P. M. Grin, L. S. Narasimhan, and S. R. Yee. Generation of uniquely encoded light patterns for range data acquisition. Pattern Recog.,1992,25(6):609-616
[76]http://www.microsoft.com/zh-cn/kinectforwindows/
[77]Mitsuo Takeda, Hideki Ina, Sejii Kobayashi. Fourier-transform method of fringe-pattern analysis of computer-based topography and interferometry. J. Opt. Soc. Am.,1982,72(1):156-160
[78]Mitsuo Takeda, Kazuhiro Mutoh. Fourier-transform profilometry for the auto measurement of 3D object shapes. Applied Optics,1983,22(24):3977-3982
[79]Su X., Chen W. Fourier transform profilometry:a review. Optics and Lasers in Engineering,2001,35(5):263-284
[80]张启灿.动态过程三维面形测量技术研究：[博士学位论文].成都：四川大学,2005
[81]Guo H., Huang P.3D shape measurement by use of a modified Fourier-transform method. In:Proceedings SPIE Vol.7066,2008
[82]Ghiglia D., Pritt M.. Two-dimensional phase unwrapping:theory, algorithms, and software. New York:Wiley,1998
[83]Su X., Chen W.. Reliability-guided phase unwrapping algorithm:a review. Optics and Lasers in Engineering,2004,42(3):245-261
[84]Su X.Y., Zhang Q.C.. Dynamic 3-d shape measurement method:A review. Opt. Laser. Eng.,2010,48(2):191-204
[85]C. Guan, L. G. Hassebrook, D. L. Lau. Composite structured light pattern for three-dimensional video. Optics Express,2003,11(5):406-417
[86]HJ. Chen, J. Zhang, D.J. Lv et al.3D shape measurement by composite pattern projection and hybrid processing. Optics Express,2007,15(19):12318-12330
[87]Jing Xu, Bingtuan Gao, Jinhua Han et al. Real-time 3D profile measurement by using the composite pattern based on the binary pattern. Optics & Laser Technology,2012, 44(3):587-593
[88]Hall-Holt O., Rusinkiewicz S.. Stripe boundary codes for real-time structured-light range scanning of moving objects. The 8th IEEE International Conference on Computer Vision,2001,359-366
[89]Rusinkiewicz S, Hall-Holt O, Levoy M. Real-time 3d model acquisition. ACM Trans. Graph.,2002,21(3):438-446
[90]Zhang L., Snavely N., Curless B., et al. Space-time Faces:High Resolution Capture for Modeling and Animation. ACM Transactions on Graphics,2004,23(3):548-558
[91]S Zhang, PS Huang. Novel method for structured light system calibration. Opt. Eng., 2006,45(8),0.83601
[92]S Zhang, X Li and S-T Yau. Multilevel quality-guided phase unwrapping algorithm for real-time 3-D shape reconstruction. Appl. Opt,2007,46(1),50-57
[93]S Zhang, PS Huang. Phase error compensation for a 3-D shape measurement system based on the phase shifting method. Opt. Eng.,2007,46(6):063601
[94]S Zhang, S-T Yau. Generic nonsinusoidal phase error correction for three-dimensional shape measurement using a digital video projector. Appl. Opt.,2007,46(1):36-43
[95]S Zhang, D Royer, S-T Yau. GPU-assisted high-resolution, real-time 3-D shape measurement. Opt. Express,2006,14(20):9120-9129
[96]Zhang S. High-resolution, real-time 3D shape measurement:[PhD Dissertation]. NewYork:Stony Brook University,2006
[97]Zhang S, Yau ST. High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method. Opt. Express.,2006,14(7):2644-2649
[98]S. Lei, S. Zhang. Digital sinusoidal fringe generation:defocusing binary patterns vs. focusing sinusoidal patterns. Opt. Laser Eng.,2010,48(5):561-569
[99]S. Zhang. Flexible 3-D shape measurement using projector defocusing:extended measurement range. Opt. Lett.,2010,35(7):931-933
[100]S. Zhang, D. van der Weide, J. Oliver. Superfast phase-shifting method for 3-D shape measurement. Opt. Express,2010,18(9):9684-9689
[101]Y. Gong, S. Zhang. Ultrafast 3-D shape measurement with an off-the-shelf DLP projector. Opt. Express,2010,18(19):19743-19754
[102]Y. Wang, S. Zhang. Optimal pulse width modulation for sinusoidal fringe generation with projector defocusing. Opt. Lett.,2010,35(24):4121-4123
[103]W. Lohry, S. Zhang.3D shape measurement with 2D area modulated binary patterns. Opt. Laser Eng.2012,50(7),917-921
[104]W. Lohry, S. Zhang. Genetic method to optimize binary dithering technique for high-quality fringe generation. Opt. Lett.,2013,38(4):540-542
[105]L. Merner, Y. Wang, S. Zhang. Accurate calibration for 3D shape measurement system using a binary defocusing technique. Opt. Laser Eng.,2013,51(5),514-519
[106]Yajun Wang, S. Zhang, James H. Oliver.3D shape measurement technique for multiple rapidly moving objects. Optics Express,2011,19(9):8539-8545
[107]Yajun Wang, S. Zhang. Superfast multi-frequency phase-shifting technique with optimal pulse with modulation. Optics Express,2011,19(6):5149-5155
[108]S. Zhang, Composite phase-shifting algorithm for absolute phase measurement. Opt. Laser Eng.,2012,50(11):1538-1541
[109]Yajun Wang, S. Zhang. Novel phase-coding method for absolute phase retrieval. Optics Letters,2012,37(11):2067-2069
[110]Kai Liu, Yongchang Wang, Daniel L. Lau, Qi Hao, Laurence G. Hassebrook. Dual-frequency pattern scheme for high-speed 3D shape measurement. Optics Express,2010,18(5):5229-5244
[111]Huntley J., Saldner H.. Shape measurement by temporal phase unwrapping: comparision of unwrapping algorithms. Measurement Science and Technology,1997, 8(9):986-992
[112]Saldner H., Huntley J. Temporal phase unwrapping:application to surface profiling of discontinuous objects. Applied optics,1997,36(13):2770-2775
[113]Huntley J., Coggrave C. Progress in phase unwrapping, Proc. SPIE 3407, International Conference on Applied Optical Metrology,1998
[114]Weise T, Leibe B, Van Gool L. Fast 3D Scanning with Automatic Motion Compensation. IEEE Conference on Computer Vision and Pattern Recognition,2007, 1(8):2462-2469
[115]Weise T, Li H, Van Gool L. Face/off:Live facial puppetry. Proceedings of the ACM SIGGRAPH,2009
[116]Yong Li, Cuifang Zhao, Yixian Qian et al. High-speed and dense three-dimensional surface acquisition using defocused binary patterns for spatially isolated objects. Optics Express,2010,18(21):21635-21642
[117]Chao Zuo, Qian Chen, Guohua Gu et al. High-speed three-dimensional profilometry for multiple objects with complex shapes. Optics Express,2012,20(17):19493-19511
[118]李中伟,王从军,史玉升.3D测量系统中的高精度摄像机标定算法.光电工程,2008,35(4)：58-63
[119]Zhongwei Li, Yusheng Shi, Congjun Wang and Yuanyuan Wang. Accurate calibration method for a structured light system. Optical Engineering,2008,47(5):053604
[120]李中伟,王从军,史玉升等.结构光测量中的高精度相位误差补偿算法,光学学报,2008,28(8)：1527-1532
[121]Zhongwei Li, Yusheng Shi, Congjun Wang, Dahui Qin, and Kui Huang. Complex Object 3D Measurement Based on Phase-Shifting and a Neural Network. Optics Communications,2009,282(14):2699-2706
[122]李中伟.基于数字光栅投影的结构光三维测量技术与系统研究：[博士学位论文].武汉：华中科技大学,2009
[123]Bing Pan, Qian Kemao, LeiHuang. Phase error analysis and compensation for nonsinusoidal waveforms in phase-shifting digital fringe projection profilometry. Optics Letter,2009,34(2):416-418
[124]Guo H., He H., Chen M.. Gamma correction for digital fringe projection profilometry. Applied Optics,2004,43(14):2906-2914
[125]M. J. Baker, J. Xi, and J. F. Chicharo. Elimination of non-linear luminance effects for digital video projection phase measuring profilometers. In 4th IEEE International Symposium on Electronic Design, Test and Applications,2008,496-501
[126]Chen X., Xi J., Jin Y. Phase error compensation method using smoothing spline approximation for a three-dimensional shape measurement system based on gray-code and phase-shift light projection. Optical Engineering,2008,47:113601
[127]Kai Liu, Yongchang Wang, Daniel L. Lau. Gamma model and its analysis for phase measuring profilometry. J. Opt. Soc. Am. A,2010,27(3):553-562
[128]Thang Hoang, Bin Pan, Dung Nguye et al. Generic gamma correction for accuracy enhacement in fringe-projection profilometry. Optics Letter,2010,35(12):1992-1994
[129]C.R. Coggrave, and J. M. Huntley. Optimization of a shape measurement system based on spatial light modulators. Opt. Eng.,2000,39(1):91-98
[130]S. Lei, S. Zhang. Flexible 3-D Shape Measurement Using Projector Defocusing. Opt. Lett.,2009,34(20):3080-3082
[131]Ke Chen, Jiangtao Xi, Yanguang Yu et al. A composite quality-guided phase unwrapping algorithm for fast 3D profile measurement, Proc. SPIE Conference, Optics & Photonics,8563,2012
[132]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision Cambridge University Press, Cambridge,2000
[133]Heikkia J., Silven O.. A four-step camera calibration procedure with implicit image correction. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997
[134]Rui Ishiyama, Shizuo Sakamoto, Johji Tajima. Absolute phase measurements using geometric constrains between multiple camreas and projectors. Appl. Opt.,2007, 46(17):3528-3538
[135]Christian Brauer-Burchardt, Christoph Munkelt, Matthias Heinze, Using geometric constrains to solve the point correspondecce problem in fringe projection based 3D measuring systems, Proc. ICIAP,2011,265-274
[136]R. Hartley, R. Gutpa, T. Chang. Stereo from uncalibrated cameras. IEEE International Conference on Computer Vision and Pattern Recognition,1992
[137]Richard I. Hartley. Theory and practice of projective rectification. International Journal of Computer Vision,1999,35(2):115-127
[138]Towers C., Towers D., Jones J.. Absolute fringe order calculation using optimized multi-frequency selection in full-field profilometry. Optics and Lasers in Engineering, 2005,43(7):788-800
[139]Faig W.. Calibration of close-range photogrammetry systems:Mathematical formulation. Photogrammetric engineering and remote sensing,1975,41(12): 1479.1486
[140]Tsai R.. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of robotics and automation,1987,3(4):323-344
[141]Zhang Z.. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334
[142]Zhang Z.. Flexible camera calibration by viewing a plane from unknown orientations. The Proceedings of the 7th IEEE International Conference on Computer Vision,1999, 1:666-673
[143]http://www.vision.caltech.edu/bouguetj/calib_doc/
[144]Zhang Z.. Camera calibration with one-dimensional objects. IEEE Transaction on pattern analysis and machine intelligence,2004,26(7):893-899
[145]Faugeras O., Luong Q., Maybank S.. Camera self-calibration:theory and experiments. Springer,1992
[146]Hartley R.. Self-calibration of stationary cameras. International Journal of Computer Vision,1997,22(1):5-23
[147]Tomas Svoboda, Daniel Martinec, and Tomas Pajdla. A convenient multi-camera self-calibration for virtual environments. PRESENCE:Teleoperators and Virtual Environments,2005,14(4):407-422
[148]Yasutaka Furukawa, Jean Ponce. Accurate camera calibration from multi-view stereo and bundle adjustment. Int. J. Comput. Vis.,2009,84(3):257-268
[149]陈厚道.面向快速成形的反求工程关键技术的研究：[硕士学位论文].武汉：华中科技大学,2003
[150]谢永辉.三维激光扫描系统的标定自动化技术与精度研究：[硕士学位论文].武汉：华中科技大学,2003
[151]江舜妹.柔性测量臂激光测量头标定及精度研究：[硕士学位论文].武汉：华中科技大学,2007
[152]杨彪.人工神经网络在反求中的应用研究：[硕士学位论文].武汉：华中科技大学,2008
[153]黄奎.多关节式坐标测量系统的关键技术研究：[博士学位论文].武汉：华中科技大学,2010
[154]Brown D. C. The bundle adjustment-progress and prospects. International Archives of Photogrammetry, ISP Congress, Helsinki,1976,21(3):1-33
[155]Bill Triggs, Philip F. McLauchlan, Richard I. Hartley. Bundle adjustment a modern synthesis. Lecture Notes in Computer Science,2000,1883:298-372
[156]V. Lepetit, F. Moreno-Noguer, P. Fua. EPnP:An accurate O(n) solution to the PnP Problem. International Journal of Computer Vision,2009,81(2):155-166
[157]Long Quan and Zhangdan Lan. Linear N-point camera pose determination. IEEE transactions on Pattern Analysis and Machine Intelligence,1999,21(8):774-780
[158]Chien-Ping Lu, Gregory D. Hager. Fast and globally convergent pose estimation from video images. IEEE transactions on Pattern Analysis and Machine Intelligence,2000, 22(6):610-622
[159]Denis Oberkampf, Daniel F. Dementhon, Larry S. Davis. Iterative pose estimation using coplanar feature points. Computer Vision and Image Understanding,1996, 63(3):495-511
[160]Gerald Schweighofer, Axel Pinz. Robust pose estimation from a planar target. IEEE transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2024-2030
[161]D.W. Eggert, A. Lorusso, R.B. Fisher. Estimating 3D rigid body transformations:a comparison of four major algorithms. Machine Vision and Applications,1997,9(5-6): 272-290
[162]张舒,诸艳利,赵开勇等.GPU高性能运算之CUDA.北京：中国水利水电出版社,2009
[163]Stivala A., Stuckey P., Wirth A. Fast and accurate protein substructure searching with simulated annealing and GPUs. BMC bioinformatics,2010,11(1):446-462
[164]Ufimtsev I.S. Marinez T.J.. Quantum chemistry on graphical processing processing units.1. strategies for two-electron integral evaluation. Journal of Chemical Theory and Computation,2008,4(2):222-231
[165]Stone J.E., Hardy D.J., Ufimtsev I.S. et al. GPU-accelerated molecular modeling coming of age. Journal of Molecular Graphics and Modeling,2010,29(2):116-125
[166]Catanzaro B., Sundaram N., Keutzer K.. Fast support vector machine training and classification on graphics processors. Proceedings of the 25th International Conference on Machine Learning, New York:ACM,2008
[167]Mamoru Miura, Kinya Fudano, Koichi Ito et al. GPU implementation of phase-based stereo correspondence and its application.19th IEEE International Conference on Image Processing,2012,1697-1700
[168]Chun-qin Shi, Liyan Zhang. A 3D shape measurement system based on random pattern projection. Fifth International Conference on Frontier of Computer Science and Technology,2010
[169]Zheng Zhang, Hock Soon Seah. CUDA acceleration of 3D dynamic scene reconstruction and 3D motion estimation for motion capture. IEEE 18th International Conference on Parallel and Distributed System,2012
[170]余进.孔周高温弹塑性疲劳变形的检测研究：[硕士学位论文].合肥：中国科学技术大学,2009
[171]于纪言.运动弹丸追踪摄影方法及管件技术研究：[博士学位论文].南京：南京理工大学,2007
[172]刘伟锋.人脸表情识别研究：[博士学位论文].合肥：中国科学技术大学,2007
[173]Amberg B., Romdhani S., Vetter T. Optimal step non-rigid ICP algorithms for surface registration. In Proceedings of IEEE Computer Vision Pattern Recognition,2007
[174]Wang M., Jenke P., Huang Q., Bokeloh M., Guibas L., Schilling A. Reconstruction of deforming geometry from time-varying point clouds. In Symposium on Geometry processing,2007,49-58
[175]Brown B. J., Rusinkiewicz S.:Global non-rigid alignment of 3D scans. ACM Trans. Graph.,2007,26(3):1-10
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