激光与光电子学进展, 2012, 49 (5): 051001, 网络出版: 2012-03-26   

基于机器视觉的三维重建技术研究 下载: 717次

Research on the Technology of Three-Dimensional Reconstruction Based on Machine Vision
作者单位
长春理工大学光电工程学院, 吉林 长春 130022
摘要
研究了基于机器视觉的三维重建技术。利用普通的数码摄像机拍摄图片,通过摄像机定标、特征点检测和匹配、基础矩阵和本质矩阵计算来实现图像的三维重建。采用张正友标定方法的相机标定工具箱实现了相机的标定,利用尺度不变特征变换(SIFT)特征点的检测和匹配方法进行了图像特征点的检测和匹配,采用RANSAC算法计算基础矩阵,最后利用相机内参数和由基础矩阵获得的本质矩阵重建物体的特征点,并进行纹理贴图。实验结果表明利用这些图像可以进行物体重建,并且能够很好地反映出物体的三维特征。
Abstract
The three-dimensional (3D) reconstruction technology based on machine vision is researched. We use the pictures shot by ordinary digital cameras to achieve 3D reconstruction through camera calibration, feature-point detection and matching, and the calculation of fundamental matrix and essential matrix. We complete the camera calibration by Camera Calibration ToolBox for Matlab based on Zhang Zhengyou′s method. The scale invariant feature transform (SIFT) method is used for image feature-points detection and matching, and the random sample consensus (RANSAC) algorithm is adopted to calculate the fundamental matrix and the essential matrix. Then we use the camera internal parameters got in advance and the essential matrix obtained from the fundamental matrix to reconstruct the feature points of the objects and achieve the 3D effect by pasting pictures of texture. The experimental results show that it can complete the reconstruction and can well reflect the 3D feature of the objects.
参考文献

[1] 陈胜勇,刘盛. 基于OpenCV的计算机视觉技术实现[M]. 北京: 科学出版社, 2008. 1~8

    Chen Shengyong, Liu Sheng. Computer Vision Technology Based on OpenCV[M]. Beijing: Science Press, 2008. 1~8

[2] 王涛,孙长库, 石永强 等. 基于辅助参考线的光栅投影轮廓测量系统及标定方法[J]. 光学学报, 2011, 31(1): 0115002

    Wang Tao, Sun Changku, Shi Yongqiang et al.. Novel grating projection system based on assistant line and its calibration method[J]. Acta Optica Sinica, 2011, 31(1): 0115002

[3] 邱志强, 梁永辉, 于起峰. 基于仿射近似从序列图像重建目标三维结构[J]. 光学学报, 2007, 27(6): 1004~1010

    Qiu Zhiqiang, Liang Yonghui, Yu Qifeng. Three-dimensional structure reconstruction from image sequence based on affine approximation[J]. Acta Optica Sinica, 2007, 27(6): 1004~1010

[4] 郭继平, 彭翔, 刘晓利 等. 条纹边界编码的动态3D重建及流水建模[J]. 光学学报, 2010, 30(10): 2884~2890

    Guo Jiping, Peng Xiang, Liu Xiaoli et al.. Dynamic 3D reconstruction and pipelined 3D modeling based on stripe boundary encoding[J]. Acta Optica Sinica, 2010, 30(10): 2884~2890

[5] 孙军华,刘震, 张广军 等. 基于柔性立体靶标的摄像机标定[J]. 光学学报, 2009, 29(12): 3433~3439

    Sun Junhua, Liu Zhen, Zhang Guangjun et al.. Camera calibration based on flexible 3D target[J]. Acta Optica Sinica, 2009, 29(12): 3433~3439

[6] H. Y. Shum, R. Szeliski, S. Baker et al.. Interactive 3D modeling from multiple images using scene regularities[C]. SMILE, 1998

[7] O. Faugeras, T. Papadopoulo. A nonlinear method for estimating the projective geometry of three views[C]. International Conference on Computer Vision, 1998. 477~484

[8] 徐巧玉,车仁生. 基于光学测棒的立体视觉坐标测量系统的研究[J]. 光学学报, 2008, 28(11): 2181~2186

    Xu Qiaoyu, Che Rensheng. Study on single camera simulating stereo vision measurement technology[J]. Acta Optica Sinica, 2008, 28(11): 2181~2186

[9] R. Y. Tsai. An efficient and accurate camera calibration technique for 3D machine vision[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1986. 364~374

[10] Z. Zhang. A flexible new technique for camera calibration[J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330~1334

[11] 刘贵喜, 刘冬梅, 刘凤鹏 等. 一种稳健的特征点配准算法[J]. 光学学报, 2008, 28(3): 454~461

    Liu Guixi, Liu Dongmei, Liu Fengpeng et al.. A robust image registration algorithm based on feature points matching[J]. Acta Optica Sinica, 2008, 28(3): 454~461

[12] F. Mokhtarian, R. Suomela. Curvature seale space for robust image corner detection[J]. Proc. International Conference on Pattern Recognition, 1998. 1819~1821

[13] C. Harris, M. Stephens. A combined corner and edge detector[C]. Fourth Alvey Vision Conference, 1988. 147~151

[14] S. M. Smith, J. M. Brady. SUSAN: a new approach to low level image proeessing[J]. International J. Computer Vision, 1997, 23(1): 45~78

[15] D. G. Lowe. Object recognition from local scale-invariant features[C] . International Conference on Computer Vision, 1999, 3(1): 1150~1157

[16] D. G. Lowe. Local feature view clustering for 3D object recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2001. 682~688

[17] S. Se, D. G. Lowe, J. Little. Global localization using distinctive visual features[C]. International Conference on Intelligent Robots and Systems, 2002. 226~231

[18] P. A. Beardsley, A. Zisserman. Affine calibration of mobile vehicles[C]. Europe-China Workshop on Geometrical Modelling and Invariants for Computer Vision, 1995, 214~221

[19] T. Vieville, D. Lingrand. Using singular displacements for uncalibrated monocular vision systems[C]. INRIA, 1995, Technical Report 2678

[20] Martin A. Fischler, Robert C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Comm. ACM, 1981, 24(6): 381~395

张宁, 常雷, 徐熙平. 基于机器视觉的三维重建技术研究[J]. 激光与光电子学进展, 2012, 49(5): 051001. Zhang Ning, Chang Lei, Xu Xiping. Research on the Technology of Three-Dimensional Reconstruction Based on Machine Vision[J]. Laser & Optoelectronics Progress, 2012, 49(5): 051001.

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