增强现实中的虚实配准方法研究
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摘要
增强现实(Augmented Reality简称AR)技术是近年来的一个研究热点,有着广泛的应用前景。它是对现实世界的补充,使得虚拟物体从感官上成为周围真实环境的组成部分。与传统的虚拟现实(Virtual Reality,简称VR)不同,增强现实只是实现对现实环境的补充而不是完全替代现实环境。增强现实技术增强了用户对现实世界的感知能力和与现实世界的交互能力。
虚实配准(三维注册)是任何增强现实系统所必须解决的关键问题。它是指系统应该能够实时精确的计算摄像机相对于真实世界的位置和姿态,以便将虚拟场景正确的安置到它应所处的位置上。基于计算机视觉的三维注册方法以其精确性高、硬件成本低廉等特点已经引起越来越多研究人员的关注。本文针对当前基于计算机视觉的三维注册技术研究现状,吸取了计算机视觉、多视图几何、非线性优化理论的最新成果,围绕三维注册中的非定标配准方法、基于标识与平面自然特征相结合的注册以及基于多平面结构自然特征的虚实注册等技术内容展开研究与实践,目标旨在提高增强现实系统可用性的同时最大限度保证注册精度和系统实时性。
本文的主要研究工作如下:
(1)针对现有基于已知标识的三维注册系统中,要求用户必须事先确定摄像机内部参数的问题。设计一种基于实时定标策略的虚实配准方法,使得系统能够在运行过程中自动求取摄像机内参,提高了三维注册系统的精确性。
(2)针对当前基于标识的开发平台在标识部分遮挡情况下无法完成三维注册问题,设计一种基于标识角点与全局单应性矩阵相结合的三维注册方法,减小帧间积累误差,增强了系统的健壮性与可用性。
(3)设计一种综合利用标识与平面自然特征的三维注册技术,在标识全部被遮挡情况下利用标识周边区域的平面自然特征点来完成注册,可以直接改进现有基于标识的三维注册方法的健壮性和可用性。
(4)分析当前各种基于自然特征的三维注册技术的优缺点,提出一种能够利用待注册场景中的多平面结构来完成注册的新方法。通过分析不同平面与视线的夹角不同以及视点与不同平面间的距离不同对最终计算结果的影响,提出一种有效的融合策略,可以较大幅度的减小抖动问题。
Augmented Reality (AR) is a growing area in virtual reality research. An augmented reality system generates a composite view for the user. It is a combination of the real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. The augmented reality presented to the user enhances that person's performance in and perception of the world. The ultimate goal is to create a system such that the user can not tell the difference between the real world and the virtual augmentation of it. To the user of this ultimate system it would appear that he is looking at a single real scene.
Registration is one of the most pivotal problems currently limiting AR applications. It means that the virtual scenes generated by computers must be aligned with the real world seamlessly. Computer vision techniques have the potential to provide the accurate registration data needed by AR systems. The Vision-based methods do not require any special and expensive equipment except for common cameras and a personal computer. These kinds of methods take full advantage of scene features including man made markers, feature points, planes, and so on, to achieve registration between real and virtual scenes. In this paper, we especially focus on the computer vision based registration approaches. We mainly investigate the uncalibrate marker based method, marker and natural feature based method and multi-planar structures based method to improve the usability and robustness of the current AR systems.
The main studies and achievements of thesis are listed as following:
(1) Marker based uncalibrate registration method
We propose a novel online dynamic calibration method to solve the problem that the intrinsic parameters of the camera can not be changed in the conventional marker based registration systems. Our method can calculate the intrinsic parameters according to the projections of the four corners in online stage and improves the usability and accuracy of the man made marker based AR systems to a large degree.
(2) Registration under the circumstance of partially occlusion in marker based AR systems
We present a new registration method based on global homography which can avoid the problem of occlusion and error accumulation in marker based Augmented Reality systems. We take following two steps to ensure the availability of our method. Firstly, a new affine invariant feature: edge-corner, is introduced to provide a robust and consistent matching primitives. Secondly, global Homography is used to calculate transformation matrix robustly by matching current and first frame to avoid error accumulation problem.
(3) Registration using marker and planar structures
We also propose a marker and planar texture based registration method. The initial registration matrix are obtained using known-size marker. The current registration matrix are calculated using homography between the current and previous frames when marker is occluded.Experiment results illuminate that our method improves the robustness and practicability of marker based registration systems particular under the circumstance of severe occlusion of man made markers.
(4) Multi-Planar structures based registration for Augmented Reality systems
We illustrate a novel registration method for augmented reality systems based on multi-planar structures and natural features tracking. The method can be divided into two stages: offline 3D reconstruction and online registration. In offline stage, the planes and world coordinate systems are established based on 3D reconstruction technique firstly. Secondly, we train the system by synthesizing a large number of views of matched features and by using PCA and K-means to generate a compact description of this view set. In online stage, we rely on this description to fulfill the task of feature matching based on nearest neighbor search method. With the obtained feature correspondences, we compute the homographies between the world planes and the current frame robustly using RANSAC and nonlinear least square method. Then, the registration matrix is synthesized using the above homographies and the transform matrices between the planes and world coordinate system obtained in offline stage. Our method cast off the requirement that the initial camera position should close to the reference images, while overcoming the problem of error accumulation. Some experiments have been carried out to demonstrate the validity of the proposed approach.
虚实配准(三维注册)是任何增强现实系统所必须解决的关键问题。它是指系统应该能够实时精确的计算摄像机相对于真实世界的位置和姿态,以便将虚拟场景正确的安置到它应所处的位置上。基于计算机视觉的三维注册方法以其精确性高、硬件成本低廉等特点已经引起越来越多研究人员的关注。本文针对当前基于计算机视觉的三维注册技术研究现状,吸取了计算机视觉、多视图几何、非线性优化理论的最新成果,围绕三维注册中的非定标配准方法、基于标识与平面自然特征相结合的注册以及基于多平面结构自然特征的虚实注册等技术内容展开研究与实践,目标旨在提高增强现实系统可用性的同时最大限度保证注册精度和系统实时性。
本文的主要研究工作如下:
(1)针对现有基于已知标识的三维注册系统中,要求用户必须事先确定摄像机内部参数的问题。设计一种基于实时定标策略的虚实配准方法,使得系统能够在运行过程中自动求取摄像机内参,提高了三维注册系统的精确性。
(2)针对当前基于标识的开发平台在标识部分遮挡情况下无法完成三维注册问题,设计一种基于标识角点与全局单应性矩阵相结合的三维注册方法,减小帧间积累误差,增强了系统的健壮性与可用性。
(3)设计一种综合利用标识与平面自然特征的三维注册技术,在标识全部被遮挡情况下利用标识周边区域的平面自然特征点来完成注册,可以直接改进现有基于标识的三维注册方法的健壮性和可用性。
(4)分析当前各种基于自然特征的三维注册技术的优缺点,提出一种能够利用待注册场景中的多平面结构来完成注册的新方法。通过分析不同平面与视线的夹角不同以及视点与不同平面间的距离不同对最终计算结果的影响,提出一种有效的融合策略,可以较大幅度的减小抖动问题。
Augmented Reality (AR) is a growing area in virtual reality research. An augmented reality system generates a composite view for the user. It is a combination of the real scene viewed by the user and a virtual scene generated by the computer that augments the scene with additional information. The augmented reality presented to the user enhances that person's performance in and perception of the world. The ultimate goal is to create a system such that the user can not tell the difference between the real world and the virtual augmentation of it. To the user of this ultimate system it would appear that he is looking at a single real scene.
Registration is one of the most pivotal problems currently limiting AR applications. It means that the virtual scenes generated by computers must be aligned with the real world seamlessly. Computer vision techniques have the potential to provide the accurate registration data needed by AR systems. The Vision-based methods do not require any special and expensive equipment except for common cameras and a personal computer. These kinds of methods take full advantage of scene features including man made markers, feature points, planes, and so on, to achieve registration between real and virtual scenes. In this paper, we especially focus on the computer vision based registration approaches. We mainly investigate the uncalibrate marker based method, marker and natural feature based method and multi-planar structures based method to improve the usability and robustness of the current AR systems.
The main studies and achievements of thesis are listed as following:
(1) Marker based uncalibrate registration method
We propose a novel online dynamic calibration method to solve the problem that the intrinsic parameters of the camera can not be changed in the conventional marker based registration systems. Our method can calculate the intrinsic parameters according to the projections of the four corners in online stage and improves the usability and accuracy of the man made marker based AR systems to a large degree.
(2) Registration under the circumstance of partially occlusion in marker based AR systems
We present a new registration method based on global homography which can avoid the problem of occlusion and error accumulation in marker based Augmented Reality systems. We take following two steps to ensure the availability of our method. Firstly, a new affine invariant feature: edge-corner, is introduced to provide a robust and consistent matching primitives. Secondly, global Homography is used to calculate transformation matrix robustly by matching current and first frame to avoid error accumulation problem.
(3) Registration using marker and planar structures
We also propose a marker and planar texture based registration method. The initial registration matrix are obtained using known-size marker. The current registration matrix are calculated using homography between the current and previous frames when marker is occluded.Experiment results illuminate that our method improves the robustness and practicability of marker based registration systems particular under the circumstance of severe occlusion of man made markers.
(4) Multi-Planar structures based registration for Augmented Reality systems
We illustrate a novel registration method for augmented reality systems based on multi-planar structures and natural features tracking. The method can be divided into two stages: offline 3D reconstruction and online registration. In offline stage, the planes and world coordinate systems are established based on 3D reconstruction technique firstly. Secondly, we train the system by synthesizing a large number of views of matched features and by using PCA and K-means to generate a compact description of this view set. In online stage, we rely on this description to fulfill the task of feature matching based on nearest neighbor search method. With the obtained feature correspondences, we compute the homographies between the world planes and the current frame robustly using RANSAC and nonlinear least square method. Then, the registration matrix is synthesized using the above homographies and the transform matrices between the planes and world coordinate system obtained in offline stage. Our method cast off the requirement that the initial camera position should close to the reference images, while overcoming the problem of error accumulation. Some experiments have been carried out to demonstrate the validity of the proposed approach.
引文
[1]石教英.虚拟现实基础及实用算法[M].北京:科学出版社, 2002
[2]阳化冰.虚拟现实构造语言VRML[M].北京:航空航天大学出版社, 2000
[3]汪成为.中国计算机学会学术著作丛书灵境(虚拟现实)技术的理论、实现及应用[M].北京:清华大学出版社, 1996
[4]朱淼良,姚远,蒋云良.增强现实综述.中国图象图形学报, 2004(9): 767-774
[5] Azuma, R. Recent advances in augmented reality. IEEE, CG&A, 2001: 340-47
[6] Milgram P, Kishino F. A Taxonomy of Mixed Reality Visual Display. IEICE Transactions on Information Systems, 1994, E77-D (12): 1321-1329
[7]孙磊.基于视觉的增强现实系统的研究: [硕士学位论文].福州大学, 2006
[8] Barfield W, Baird K, Shewchuk J, and Ioannou G. Applications of Wearable Computers and Augmented Reality to Manufacturing. Fundamentals of Wearable Computers and Augmented Reality, 2001: 695-724
[9] MacIntyre B, Lohse M, Bolter J. D, and Moreno E. Integrating 2-D Video Actors into 3-D Augmented-Reality Systems. Presence, 2002, 11(2): 189-20
[10] Dorner R, Geiger C, Haller M, et al. Authoring mixed reality-a component and framework-based approach. International Workshop on Entertainment Computing- Special Session on Mixed Reality Entertainment Computing, 2002: 405-413
[11] Julier S, Lanzagorta M, Baillot Y, Rosenblum L, and Feiner S. Information Filtering for Mobile Augmented Reality. IEEE Int’l Symp on Augmented Reality, 2000: 3-11
[12] Piekarski W, Thomas B H. Augmented reality working planes: a foundation for action and construction at a distance. The Third IEEE and ACM Int’l Symposium on Mixed and Augmented Reality, 2004: 162-171
[13] Rosenthal M, State A, Lee H, Hirota G, Ackerman J. Augmented Reality Guidance for Needle Biopsies: An Initial Randomized, Controlled Trial in Phantoms, Medical Image Analysis, 2002, 6(3): 313-320
[14]段会龙.基于增强现实的外科手术导航技术.中国医疗器械杂志, 2004
[15] State A, Hirota G, Chen D T, et al. Superior augmented reality registration by integrating landmark tracking and magnetic tracking. Proceedings of SIGGRAPH, 1996: 429-438
[16] Auer T, Pinz A. The integration of optical and magnetic tracking for multi-user augmented reality. Computers. &. Graphics, 2003, 23(6): 805-808
[17] Ghazisaedy M, Adamczyk D, Sandin D, Kenyon R, Defanti T. Ultrasonic Calibration of a Magnetic Tracker in a Virtual Reality Space. Proceedings of the Virtual Reality Annual International Symposium '95, IEEE Computer Society, 1995: 179-188
[18] Zsolt Szalavári, Erik Eckstein, Michael Gervautz. Collaborative Gaming in Augmented Reality, Proceedings of VRST'98, 1998: 195-204
[19] Vallino, James Richard. Interactive augmented reality. PhD thesis, The University of Rochester, 1998
[20] Pasman W, Woodward C. Implementation of an Augmented Reality System on a PDA, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003: 276-277
[21] http: // studierstube. icg. tu-graz. ac. at/
[22] Lepetit V, and Berger M. O. A semi-automatic method for resolving occlusion in augmented reality. In Proc. of CVPR, 2000: 225-230
[23] Lepetit V, and Berger M. O. Handling Occlusion in Augmented Reality Systems: A Semi-Automatic Method. In Proc. of ISAR, 2000: 137-146
[24] Schmidt J, H. Niemann, Vogt S. Dense Disparity Maps in Real Time with an Application to Augmented Reality. Proceedings Sixth IEEE Workshop on Applications of Computer Vision, 2002: 225-230
[25] Kim H, Yang S, Sohn K. 3D reconstruction of stereo images for interaction between real and virtual worlds. In: Proc. of the ISMAR, 2003: 167-177
[26] Hayashi K, Kato H, Nishida S. Occlusion Detection of Real Objects using Contour Based Stereo Matching. In: Proc. of the ICAT, 2005: 180-186
[27] Kutulakos K. N, and Vallino J. R. Calibration-Free Augmented Reality, IEEE Transactions on Visualization and Computer Graphics, 1998, 4(1): 1-20
[28]明德烈,柳健,田金文.增强现实中的虚实注册技术研究.中国图象图形学报, 2003, 8(5): 557-561
[29] Yuan M. L, Ong S. K, and Nee A. Y. C. Registration Based on Projective Reconstruction Technique for Augmented Reality Systems. IEEE Trans. on Visualization and Computer Graphics, 2005, 11(3): 254-264
[30] Yuan M. L, Ong S. K, and Nee A. Y. C. Registration using natural features for augmented reality systems. IEEE Trans. on Visualization and Computer Graphics, 2006, 12(4): 569-580
[31]周雅,徐彤,阎达远等.增强现实中虚拟物体的投影注册算法研究.北京理工大学学报, 2000, 6: 738-742
[32]陈靖,王涌天,施琦.增强现实系统的动态注册,北京理工大学学报, 2001, 5: 631-636
[33] Sinclair P. Integrating Hypermedia Techniques with Augmented Reality Environments. PhD thesis, Univ. of Southampton, 2004
[34] Seo Y. D, and Hong K. S. Calibration-Free Augmented Reality in Perspective. IEEE Trans. Visualization and Computer Graphics, 2000, 6(4): 346-359
[35] http: //www. hitl. washington. edu/artoolkit/
[36] Simon G. and Berger M. Reconstructing while registering: a novel approach for markerless augmented reality. In Proc. of ISMAR, 2002: 285-294
[37] Simon G. and Berger M. Real time registration known or recovered multi-planar structures: application to ar. In Proc. of BMVC, 2002: 567-576
[38] Hartley R, and Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2004
[39] Tsai R Y. An efficient and accurate camera calibration technique for 3D machine vision, In: Proc. CVPR, 1986: 364-374
[40] Longuet-Higgins H. C. A computer algorithm for reconstructing a scene from two projections, Nature, 1981, 293: 133-135
[41] Luong, Q. -T. and Faugeras, O. D. The fundamental matrix: Theory, algorithms and stability analysis. IJCV, 1996, 1(17): 43-76
[42] Lepetit V. and Fua P. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey. Foundations and Trends in Computer Graphics and Vision, 2005, 1(1): 1-89
[43] Emanuele T, Alessandro V. Introductory techniques for 3D computer vision. Prentice-Hall, 1998
[44] Arun K, Huang T, and Blostein S. Least-squares fitting of two 3-D points sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5): 698-700
[45] Horn B, Hilden H, and Negahdaripour S. Closed-form solution of absolute orientation using orthonormal matrices. Journal of the Optical Society of America, 1987, 5(7): 1127-1135
[46] Press W, Teukoisky S, Vetterling W, and Flannery B. Numerical recipes in C. Cambridge University Press, 1992
[47] Triggs B, McLauchlan P, Hartley R, and Fitzgibbon A. Bundle adjustment -a modern synthesis. In Vision Algorithms: Theory and Practice, Springer-Verlag, 2000: 298-372
[48] Reitmayr G, and Drummond T. Going out: Robust model based tracking for outdoor augmented reality. In Proc. IEEE International Symposium on Mixed and Augmented Reality, 2006: 109-118
[49] Chekhlov D, Pupilli M, Mayol-Cuevas W, and Calway A. Real-time and robust monocular SLAM using predictive multi-resolution descriptors. In Proc. 2nd International Symposium on Visual Computing, ISVC, 2006: 276-285
[50] Davison A, Reid I, Molton N, and Stasse O. MonoSLAM: Real-time single cameraSLAM. IEEE Trans. Pattern Analysis and Machine Intelligence, 2007, 29(6): 1052-1067
[51] Rosten E, and Drummond T. Fusing points and lines for high performance tracking. In Proc. IEEE International Conference on Computer Vision, 2005: 1508-1511
[52] Rosten E, and Drummond T. Scalable monocular SLAM. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2006: 469-476
[53] Jiang, B., You, S., Neumann, U. A Robust Tracking System for Outdoor Augmented Reality. Proceedings of IEEE Virtual Reality, 2004: 3-10
[54] Jain S, and Neumann U. Real-time Camera Pose and Focal Length Estimation. Pattern Recognition, 2006, 1: 551-555
[55] Simon G, and Berger M. O. Registration with a moving zoom lens camera for augmented reality applications. In Proc. 6th European Conference on Computer Vision, Dublin, Ireland, 2000: 578-594
[56]张浩.基于单目视觉的增强现实技术研究: [硕士学位论文].华中科技大学, 2004
[57] Vincent L, And Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 1991, 13 (6): 583-598
[58]白燕斌等. OpenGL三维图形库编程指南[M].北京:机械工业出版社, 1998
[59]吴斌等. OpenGL编程实例与技巧[M].北京:人民邮电出版社, 1999
[60] OpenCV: http: //www. intel. com/research/mrl/research/opencv
[61]周雅,闫达远.增强现实系统显示技术探讨.计算机工程与应用, 2003: 35-38
[62]施琦,王涌天,陈靖.一种基于视觉的增强现实三维注册算法.中国图像图形学报, 2002: 679-683
[63] Shahzad Malik, Chris McDonald, Gerhard Roth. Hand Tracking for Interactive Pattern-based Augmented Reality. In Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality, 2002: 117-126
[64] Shahzad Malik, Gerhard Roth, Chris McDonald. Robust 2D Tracking for Real-time Augmented Reality. In Proceedings of Vision Interface, 2002: 399-406
[65]顾耀林,毛健.一种基于平面模板的虚实配准算法,中国图象图形学报, 2006, 8: 1175-1179
[66] Fischler M. A, and Bolles R. C. RANdom SAmple Consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381-395
[67] Matas J, and Chum O. Randomized RANSAC with td, d test. Image and Vision Computing, 2004, 22(10): 837-842
[68] Harris C, and Stephens M. A Combined Corner and Edge Detector. In Proc. of Alvey Vision, 1988, 189-192
[69] SMITH S M, BRADY M. SUSAN—A new approach to low level image processing[J]. International Jaurnal of Computer Vision, 1997, 23(1): 45-78
[70] Gordon I, and Lowe D. G. What and where: 3D object recognition with accurate pose. In Toward Category-Level Object Recognition, Springer-Verlag, 2006: 67-82
[71] Hough VC, Method and Means for Recognizing Complex Patterns, U. S., Patent, 1962
[72] Kiryati N, Eldar Y, Bruckstein A M. A probabilistic Hough transform. Pattern Recognition, 1991, 24(4): 303-316
[73] Xu L, Oja E, Kultanen P. A new curve detection method: Randomized Hough Transform. Pattern Recognition Letters, 1990(11): 331 - 338
[74]王晓冬,霍宏,方涛.基于快速归一化互相关函数的运动车辆阴影检测算法.计算机应用, 2006, 26(9): 2065-2067
[75] Giachetti A. Matching techniques to compute image motion. Image and Vision Computing, 2000(18): 247-260
[76]陈金波.基于自然特征点的AR注册方法的研究: [硕士学位论文].上海大学, 2005
[77] Bruce D. Lucas and Takeo Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence, 1981: 674-679
[78] Carlo Tomasi and Takeo Kanade. Detection and Tracking of Point Features. Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991
[79] Jianbo Shi and Carlo Tomasi. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition, 1994: 593-600
[80] Jin, H., Favaro, P., Soatto, S.: Real-Time Feature Tracking and Outlier Rejection with Changes in Illumination. In: Proceedings of the International Conference on Computer Vision, Vancouver, Canada, 2001: 684-689
[81] Comport. A and Marchand. E. A real-time tracker for markerless augmented reality. In ACM/IEEE Int. Symp. on Mixed and Augmented Reality, 2003: 36-45
[82] Neumann U, and You S. Natural feature tracking for augmented reality, IEEE Trans. on Multimadia, 1999, 1(1): 53-64
[83] Chia K. W, Cheok A, and Prince S. J. D. Online 6 dof augmented reality registration from natural features. In Proc. of the IEEE and ACM ISMAR, 2002: 305-313
[84] Hager G, Belhumeur P. Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998: 1025-1039
[85] Jurie F, Dhome M, Real time robust template matching. In The 13th British Machine Vision Conference, 2002: 123-132
[86] Jaynes C, Hou J, Temporal registration using a kalman filter for augmented reality. In Vision Interface, 2000
[87] Simon G, and Berger M. A two-stage robust statistical method for temporal registration from features of various type, In Proc. of 6th International Conference on Computer Vision, 1998: 261-266
[88] Simon G, Fitzgibbon A, and Zisserman A. Markerless tracking using planar structures in the scene, In Proc. IEEE and ACM ISAR, 2000: 120-128
[89] Simon G. Accurate Camera Calibation for Off-line, Video-Based Augmented Reality. IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2002), Darmstadt, Germany, September 2002
[90] Simon G. ICARUS: Interactive Reconstruction from Uncalibrated Image Sequences. ACM Siggraph 2002 Conference Abstracts and Applications. San Antonio, Texas, July, 2002
[91] Uematsu Y, and Saito H. Vision based registration for augmented reality using multi-planes in arbitrary position and pose by moving uncalibrated camera. In Proc. of Mirage, 2005: 111-119
[92] Drummond T, and Cipolla R. Real-time tracking of complex structures with on-line camera calibration, In Proc. of the BMVC, 1999: 574-583
[93] Comport A. I, and Marchand F. Robust Real-Time Visual Tracking: Comparison, Theoretical Analysis and Performance Evaluation, IEEE Int. Conf. on Robotics and Automation, 2005
[94] Klein, K., Drummond, T. Sensor fusion and occlusion refinement for tablet-based ar. In: Proc. of the ISMAR, 2004: 38-47
[95] Tom Drummond. Real-Time Tracking Of Complex Structures With On-Line Camera Calibration. BMVC Workshop on Vision Algorithms, 1999
[96] Wuest H, Vial F, and Stricker D. Adaptive line tracking with multiple hypotheses for augmented reality. In ISMAR, 2005: 62-69
[97] Bartoli A, Tunzelmann E, and Zisserman A. Augmenting images of non-rigid scenes using point and curve correspondences. In Proc. of CVPR, 2004, 1(1): 699-706
[98] Comport A. I, Marchand E, and Chaumette F. A real time tracker for markerless augmented reality. In Proc. of ISMAR, 2003: 36-45
[99] Ferrari V, Tuytelaars T, and Gool L. V, Makerless augmented reality with a real-time affine region tracker, In Proc. of the ISAR, 2001: 87-96
[100] Vacchetti L, Lepetit V, and Fua P, Stable Real-Time 3D Tracking Using Online and Offline Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(10): 1391-1391
[101] Vacchetti L, Lepetit V, and Fua P, Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking. In Proceedings of International Symposium on Mixed and Augmented Reality, 2004
[102] Vacchetti L, Lepetit V, and Fua P, Fusing Online and Offline Information for Stable 3D Tracking in Real-Time. In Proceedings of Conference on Computer Vision and Pattern Recognition, Madison, 2003
[103] Pang Y, Nee AYC, Ong SK, Yuan ML. Assembly feature design in an augmented reality environment. Assembly Automation, 2006, 26(1): 34-43
[104] Pang Y, Nee AYC, Ong SK, Yuan ML. A Markerless Registration Method for Augmented Reality based on Affine Properties. In Proc. of AUIC, 2006: 25-32
[105] Davison A. J. Real-time simultaneous localisation and mapping with a single camera. In Proc. International Conference on Computer Vision, 2003
[106] Davison A. J. Real-time 3D SLAM with wide-angle vision. In Proc. IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon, July 2004
[107] Williams, B. and Klein, G. and Reid, I Real-time SLAM Relocalisation. Proc. International Conference on Computer Vision, 2007
[108] Mark Pupilli and Andrew Calway. Real-time Camera Tracking Using Known 3D Models and a Particle Filter. In: International Conference on Pattern Recognition, 2006
[109] Mark Pupilli and Andrew Calway. Real-time Visual SLAM with Resilience to Erratic Motion. In: IEEE Computer Vision and Pattern Recognition, 2006
[110] Mark Pupilli and Andrew Calway. Particle Filtering for Robust Single CameraLocalisation. In: First International Workshop on Mobile Vision, 2006
[111] Longuet-Higgins H. C. A computer program for reconstructing a scene from two projections. Nature, 1981, 293: 133-135
[112] Weng J. Y. Motion and structure from two perspective views: algorithms, error analysis and error estimation. IEEE Trans. PAMI, 1989, 11(5): 451-476
[113] Hartley R. I. Estimation of relative camera positions for uncalibrated cameras. Proc. of ECCV, LNCS-Series 1992, 588: 579-587
[114] Lepetit V, Pilet J, and Fua P. Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004
[115] Lepetit V, Pilet J, and Fua P. Randomized Trees for Real-Time Keypoint Recognition. In Proceedings of Conference on Computer Vision and Pattern Recognition, San Diego, CA, June 2005
[116] Lepetit V, Fua P. Keypoint Recognition using Randomized Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1465-1479
[117] Zhang Z. A flexible new technique for camera calibration. IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334
[2]阳化冰.虚拟现实构造语言VRML[M].北京:航空航天大学出版社, 2000
[3]汪成为.中国计算机学会学术著作丛书灵境(虚拟现实)技术的理论、实现及应用[M].北京:清华大学出版社, 1996
[4]朱淼良,姚远,蒋云良.增强现实综述.中国图象图形学报, 2004(9): 767-774
[5] Azuma, R. Recent advances in augmented reality. IEEE, CG&A, 2001: 340-47
[6] Milgram P, Kishino F. A Taxonomy of Mixed Reality Visual Display. IEICE Transactions on Information Systems, 1994, E77-D (12): 1321-1329
[7]孙磊.基于视觉的增强现实系统的研究: [硕士学位论文].福州大学, 2006
[8] Barfield W, Baird K, Shewchuk J, and Ioannou G. Applications of Wearable Computers and Augmented Reality to Manufacturing. Fundamentals of Wearable Computers and Augmented Reality, 2001: 695-724
[9] MacIntyre B, Lohse M, Bolter J. D, and Moreno E. Integrating 2-D Video Actors into 3-D Augmented-Reality Systems. Presence, 2002, 11(2): 189-20
[10] Dorner R, Geiger C, Haller M, et al. Authoring mixed reality-a component and framework-based approach. International Workshop on Entertainment Computing- Special Session on Mixed Reality Entertainment Computing, 2002: 405-413
[11] Julier S, Lanzagorta M, Baillot Y, Rosenblum L, and Feiner S. Information Filtering for Mobile Augmented Reality. IEEE Int’l Symp on Augmented Reality, 2000: 3-11
[12] Piekarski W, Thomas B H. Augmented reality working planes: a foundation for action and construction at a distance. The Third IEEE and ACM Int’l Symposium on Mixed and Augmented Reality, 2004: 162-171
[13] Rosenthal M, State A, Lee H, Hirota G, Ackerman J. Augmented Reality Guidance for Needle Biopsies: An Initial Randomized, Controlled Trial in Phantoms, Medical Image Analysis, 2002, 6(3): 313-320
[14]段会龙.基于增强现实的外科手术导航技术.中国医疗器械杂志, 2004
[15] State A, Hirota G, Chen D T, et al. Superior augmented reality registration by integrating landmark tracking and magnetic tracking. Proceedings of SIGGRAPH, 1996: 429-438
[16] Auer T, Pinz A. The integration of optical and magnetic tracking for multi-user augmented reality. Computers. &. Graphics, 2003, 23(6): 805-808
[17] Ghazisaedy M, Adamczyk D, Sandin D, Kenyon R, Defanti T. Ultrasonic Calibration of a Magnetic Tracker in a Virtual Reality Space. Proceedings of the Virtual Reality Annual International Symposium '95, IEEE Computer Society, 1995: 179-188
[18] Zsolt Szalavári, Erik Eckstein, Michael Gervautz. Collaborative Gaming in Augmented Reality, Proceedings of VRST'98, 1998: 195-204
[19] Vallino, James Richard. Interactive augmented reality. PhD thesis, The University of Rochester, 1998
[20] Pasman W, Woodward C. Implementation of an Augmented Reality System on a PDA, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003: 276-277
[21] http: // studierstube. icg. tu-graz. ac. at/
[22] Lepetit V, and Berger M. O. A semi-automatic method for resolving occlusion in augmented reality. In Proc. of CVPR, 2000: 225-230
[23] Lepetit V, and Berger M. O. Handling Occlusion in Augmented Reality Systems: A Semi-Automatic Method. In Proc. of ISAR, 2000: 137-146
[24] Schmidt J, H. Niemann, Vogt S. Dense Disparity Maps in Real Time with an Application to Augmented Reality. Proceedings Sixth IEEE Workshop on Applications of Computer Vision, 2002: 225-230
[25] Kim H, Yang S, Sohn K. 3D reconstruction of stereo images for interaction between real and virtual worlds. In: Proc. of the ISMAR, 2003: 167-177
[26] Hayashi K, Kato H, Nishida S. Occlusion Detection of Real Objects using Contour Based Stereo Matching. In: Proc. of the ICAT, 2005: 180-186
[27] Kutulakos K. N, and Vallino J. R. Calibration-Free Augmented Reality, IEEE Transactions on Visualization and Computer Graphics, 1998, 4(1): 1-20
[28]明德烈,柳健,田金文.增强现实中的虚实注册技术研究.中国图象图形学报, 2003, 8(5): 557-561
[29] Yuan M. L, Ong S. K, and Nee A. Y. C. Registration Based on Projective Reconstruction Technique for Augmented Reality Systems. IEEE Trans. on Visualization and Computer Graphics, 2005, 11(3): 254-264
[30] Yuan M. L, Ong S. K, and Nee A. Y. C. Registration using natural features for augmented reality systems. IEEE Trans. on Visualization and Computer Graphics, 2006, 12(4): 569-580
[31]周雅,徐彤,阎达远等.增强现实中虚拟物体的投影注册算法研究.北京理工大学学报, 2000, 6: 738-742
[32]陈靖,王涌天,施琦.增强现实系统的动态注册,北京理工大学学报, 2001, 5: 631-636
[33] Sinclair P. Integrating Hypermedia Techniques with Augmented Reality Environments. PhD thesis, Univ. of Southampton, 2004
[34] Seo Y. D, and Hong K. S. Calibration-Free Augmented Reality in Perspective. IEEE Trans. Visualization and Computer Graphics, 2000, 6(4): 346-359
[35] http: //www. hitl. washington. edu/artoolkit/
[36] Simon G. and Berger M. Reconstructing while registering: a novel approach for markerless augmented reality. In Proc. of ISMAR, 2002: 285-294
[37] Simon G. and Berger M. Real time registration known or recovered multi-planar structures: application to ar. In Proc. of BMVC, 2002: 567-576
[38] Hartley R, and Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2004
[39] Tsai R Y. An efficient and accurate camera calibration technique for 3D machine vision, In: Proc. CVPR, 1986: 364-374
[40] Longuet-Higgins H. C. A computer algorithm for reconstructing a scene from two projections, Nature, 1981, 293: 133-135
[41] Luong, Q. -T. and Faugeras, O. D. The fundamental matrix: Theory, algorithms and stability analysis. IJCV, 1996, 1(17): 43-76
[42] Lepetit V. and Fua P. Monocular Model-Based 3D Tracking of Rigid Objects: A Survey. Foundations and Trends in Computer Graphics and Vision, 2005, 1(1): 1-89
[43] Emanuele T, Alessandro V. Introductory techniques for 3D computer vision. Prentice-Hall, 1998
[44] Arun K, Huang T, and Blostein S. Least-squares fitting of two 3-D points sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5): 698-700
[45] Horn B, Hilden H, and Negahdaripour S. Closed-form solution of absolute orientation using orthonormal matrices. Journal of the Optical Society of America, 1987, 5(7): 1127-1135
[46] Press W, Teukoisky S, Vetterling W, and Flannery B. Numerical recipes in C. Cambridge University Press, 1992
[47] Triggs B, McLauchlan P, Hartley R, and Fitzgibbon A. Bundle adjustment -a modern synthesis. In Vision Algorithms: Theory and Practice, Springer-Verlag, 2000: 298-372
[48] Reitmayr G, and Drummond T. Going out: Robust model based tracking for outdoor augmented reality. In Proc. IEEE International Symposium on Mixed and Augmented Reality, 2006: 109-118
[49] Chekhlov D, Pupilli M, Mayol-Cuevas W, and Calway A. Real-time and robust monocular SLAM using predictive multi-resolution descriptors. In Proc. 2nd International Symposium on Visual Computing, ISVC, 2006: 276-285
[50] Davison A, Reid I, Molton N, and Stasse O. MonoSLAM: Real-time single cameraSLAM. IEEE Trans. Pattern Analysis and Machine Intelligence, 2007, 29(6): 1052-1067
[51] Rosten E, and Drummond T. Fusing points and lines for high performance tracking. In Proc. IEEE International Conference on Computer Vision, 2005: 1508-1511
[52] Rosten E, and Drummond T. Scalable monocular SLAM. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2006: 469-476
[53] Jiang, B., You, S., Neumann, U. A Robust Tracking System for Outdoor Augmented Reality. Proceedings of IEEE Virtual Reality, 2004: 3-10
[54] Jain S, and Neumann U. Real-time Camera Pose and Focal Length Estimation. Pattern Recognition, 2006, 1: 551-555
[55] Simon G, and Berger M. O. Registration with a moving zoom lens camera for augmented reality applications. In Proc. 6th European Conference on Computer Vision, Dublin, Ireland, 2000: 578-594
[56]张浩.基于单目视觉的增强现实技术研究: [硕士学位论文].华中科技大学, 2004
[57] Vincent L, And Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 1991, 13 (6): 583-598
[58]白燕斌等. OpenGL三维图形库编程指南[M].北京:机械工业出版社, 1998
[59]吴斌等. OpenGL编程实例与技巧[M].北京:人民邮电出版社, 1999
[60] OpenCV: http: //www. intel. com/research/mrl/research/opencv
[61]周雅,闫达远.增强现实系统显示技术探讨.计算机工程与应用, 2003: 35-38
[62]施琦,王涌天,陈靖.一种基于视觉的增强现实三维注册算法.中国图像图形学报, 2002: 679-683
[63] Shahzad Malik, Chris McDonald, Gerhard Roth. Hand Tracking for Interactive Pattern-based Augmented Reality. In Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality, 2002: 117-126
[64] Shahzad Malik, Gerhard Roth, Chris McDonald. Robust 2D Tracking for Real-time Augmented Reality. In Proceedings of Vision Interface, 2002: 399-406
[65]顾耀林,毛健.一种基于平面模板的虚实配准算法,中国图象图形学报, 2006, 8: 1175-1179
[66] Fischler M. A, and Bolles R. C. RANdom SAmple Consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381-395
[67] Matas J, and Chum O. Randomized RANSAC with td, d test. Image and Vision Computing, 2004, 22(10): 837-842
[68] Harris C, and Stephens M. A Combined Corner and Edge Detector. In Proc. of Alvey Vision, 1988, 189-192
[69] SMITH S M, BRADY M. SUSAN—A new approach to low level image processing[J]. International Jaurnal of Computer Vision, 1997, 23(1): 45-78
[70] Gordon I, and Lowe D. G. What and where: 3D object recognition with accurate pose. In Toward Category-Level Object Recognition, Springer-Verlag, 2006: 67-82
[71] Hough VC, Method and Means for Recognizing Complex Patterns, U. S., Patent, 1962
[72] Kiryati N, Eldar Y, Bruckstein A M. A probabilistic Hough transform. Pattern Recognition, 1991, 24(4): 303-316
[73] Xu L, Oja E, Kultanen P. A new curve detection method: Randomized Hough Transform. Pattern Recognition Letters, 1990(11): 331 - 338
[74]王晓冬,霍宏,方涛.基于快速归一化互相关函数的运动车辆阴影检测算法.计算机应用, 2006, 26(9): 2065-2067
[75] Giachetti A. Matching techniques to compute image motion. Image and Vision Computing, 2000(18): 247-260
[76]陈金波.基于自然特征点的AR注册方法的研究: [硕士学位论文].上海大学, 2005
[77] Bruce D. Lucas and Takeo Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence, 1981: 674-679
[78] Carlo Tomasi and Takeo Kanade. Detection and Tracking of Point Features. Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991
[79] Jianbo Shi and Carlo Tomasi. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition, 1994: 593-600
[80] Jin, H., Favaro, P., Soatto, S.: Real-Time Feature Tracking and Outlier Rejection with Changes in Illumination. In: Proceedings of the International Conference on Computer Vision, Vancouver, Canada, 2001: 684-689
[81] Comport. A and Marchand. E. A real-time tracker for markerless augmented reality. In ACM/IEEE Int. Symp. on Mixed and Augmented Reality, 2003: 36-45
[82] Neumann U, and You S. Natural feature tracking for augmented reality, IEEE Trans. on Multimadia, 1999, 1(1): 53-64
[83] Chia K. W, Cheok A, and Prince S. J. D. Online 6 dof augmented reality registration from natural features. In Proc. of the IEEE and ACM ISMAR, 2002: 305-313
[84] Hager G, Belhumeur P. Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998: 1025-1039
[85] Jurie F, Dhome M, Real time robust template matching. In The 13th British Machine Vision Conference, 2002: 123-132
[86] Jaynes C, Hou J, Temporal registration using a kalman filter for augmented reality. In Vision Interface, 2000
[87] Simon G, and Berger M. A two-stage robust statistical method for temporal registration from features of various type, In Proc. of 6th International Conference on Computer Vision, 1998: 261-266
[88] Simon G, Fitzgibbon A, and Zisserman A. Markerless tracking using planar structures in the scene, In Proc. IEEE and ACM ISAR, 2000: 120-128
[89] Simon G. Accurate Camera Calibation for Off-line, Video-Based Augmented Reality. IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2002), Darmstadt, Germany, September 2002
[90] Simon G. ICARUS: Interactive Reconstruction from Uncalibrated Image Sequences. ACM Siggraph 2002 Conference Abstracts and Applications. San Antonio, Texas, July, 2002
[91] Uematsu Y, and Saito H. Vision based registration for augmented reality using multi-planes in arbitrary position and pose by moving uncalibrated camera. In Proc. of Mirage, 2005: 111-119
[92] Drummond T, and Cipolla R. Real-time tracking of complex structures with on-line camera calibration, In Proc. of the BMVC, 1999: 574-583
[93] Comport A. I, and Marchand F. Robust Real-Time Visual Tracking: Comparison, Theoretical Analysis and Performance Evaluation, IEEE Int. Conf. on Robotics and Automation, 2005
[94] Klein, K., Drummond, T. Sensor fusion and occlusion refinement for tablet-based ar. In: Proc. of the ISMAR, 2004: 38-47
[95] Tom Drummond. Real-Time Tracking Of Complex Structures With On-Line Camera Calibration. BMVC Workshop on Vision Algorithms, 1999
[96] Wuest H, Vial F, and Stricker D. Adaptive line tracking with multiple hypotheses for augmented reality. In ISMAR, 2005: 62-69
[97] Bartoli A, Tunzelmann E, and Zisserman A. Augmenting images of non-rigid scenes using point and curve correspondences. In Proc. of CVPR, 2004, 1(1): 699-706
[98] Comport A. I, Marchand E, and Chaumette F. A real time tracker for markerless augmented reality. In Proc. of ISMAR, 2003: 36-45
[99] Ferrari V, Tuytelaars T, and Gool L. V, Makerless augmented reality with a real-time affine region tracker, In Proc. of the ISAR, 2001: 87-96
[100] Vacchetti L, Lepetit V, and Fua P, Stable Real-Time 3D Tracking Using Online and Offline Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(10): 1391-1391
[101] Vacchetti L, Lepetit V, and Fua P, Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking. In Proceedings of International Symposium on Mixed and Augmented Reality, 2004
[102] Vacchetti L, Lepetit V, and Fua P, Fusing Online and Offline Information for Stable 3D Tracking in Real-Time. In Proceedings of Conference on Computer Vision and Pattern Recognition, Madison, 2003
[103] Pang Y, Nee AYC, Ong SK, Yuan ML. Assembly feature design in an augmented reality environment. Assembly Automation, 2006, 26(1): 34-43
[104] Pang Y, Nee AYC, Ong SK, Yuan ML. A Markerless Registration Method for Augmented Reality based on Affine Properties. In Proc. of AUIC, 2006: 25-32
[105] Davison A. J. Real-time simultaneous localisation and mapping with a single camera. In Proc. International Conference on Computer Vision, 2003
[106] Davison A. J. Real-time 3D SLAM with wide-angle vision. In Proc. IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon, July 2004
[107] Williams, B. and Klein, G. and Reid, I Real-time SLAM Relocalisation. Proc. International Conference on Computer Vision, 2007
[108] Mark Pupilli and Andrew Calway. Real-time Camera Tracking Using Known 3D Models and a Particle Filter. In: International Conference on Pattern Recognition, 2006
[109] Mark Pupilli and Andrew Calway. Real-time Visual SLAM with Resilience to Erratic Motion. In: IEEE Computer Vision and Pattern Recognition, 2006
[110] Mark Pupilli and Andrew Calway. Particle Filtering for Robust Single CameraLocalisation. In: First International Workshop on Mobile Vision, 2006
[111] Longuet-Higgins H. C. A computer program for reconstructing a scene from two projections. Nature, 1981, 293: 133-135
[112] Weng J. Y. Motion and structure from two perspective views: algorithms, error analysis and error estimation. IEEE Trans. PAMI, 1989, 11(5): 451-476
[113] Hartley R. I. Estimation of relative camera positions for uncalibrated cameras. Proc. of ECCV, LNCS-Series 1992, 588: 579-587
[114] Lepetit V, Pilet J, and Fua P. Point Matching as a Classification Problem for Fast and Robust Object Pose Estimation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004
[115] Lepetit V, Pilet J, and Fua P. Randomized Trees for Real-Time Keypoint Recognition. In Proceedings of Conference on Computer Vision and Pattern Recognition, San Diego, CA, June 2005
[116] Lepetit V, Fua P. Keypoint Recognition using Randomized Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1465-1479
[117] Zhang Z. A flexible new technique for camera calibration. IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334
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