基于未定标图像的三维模型重建
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摘要
基于图像的建模技术(image-based modeling，IBM)是近年来兴起的基于图像的图形学方法的重要研究内容，也是计算机视觉的重要研究领域。该技术基于数码相机、数码摄像机等各种高性能数字化设备拍摄得到的数字图像，提取结构信息，重构场景或物体的三维几何模型。
未定标图像的三维模型重建技术是IBM中的一个重要分支，是指只需要场景或物体的不同视点序列图像就可以重构出场景或物体的三维几何模型。本文对该技术的研究包括立体匹配、相机自定标、特征点三维重建、纹理映射等内容。
立体匹配是计算机视觉研究的一项重要课题，本文研究了一种基于角点提取的立体匹配算法。首先使用最小同值分割吸收核(SUSAN)方法判断左右图像的边缘点是否为角点。然后根据角点间的特征相似度，建立起左右图像中被保留角点的匹配关系。利用加权归一算法估计基础矩阵的基础上，引入逐次去除异常匹配点，进行迭代计算，对基础矩阵求精。
相机自定标及对应特征点三维重建是三维重建技术中的关键问题。相机自定标包括相机内参数和相机外参数计算。本文根据基础矩阵估计相机焦距的方法，引入半定标矩阵来计算相机内参数；根据本质矩阵来计算相机的外参数。对应特征点的三维重建是根据三角测量的方法计算其投影矩阵，然后用奇异值分解求出特征点的三维齐次坐标。
本文中的纹理映射是将二维图像的纹理贴到三维框架表面，从而提高模型的可视性，达到“照片级”的视觉效果。本文以特征点为节点对二维图像进行三角剖分，然后将每个三角形纹理图像一一对应地贴到重建的三维模型表面。
基于上述研究，本文开发了一个基于未定标图像的三维模型重建系统，包括自动与互动方式相结合的图像角点提取及匹配，基础矩阵的估计及求精，相机的自定标，特征点三维重建和纹理映射等模块，能够得到较好的三维模型重建结果。
The technique of image-based modeling is important content in computer graphics and computer vision. This technique can extract information of construction, reconstruct 3D model from digital images which captured by DC, DV and some other digital equipments.
The technique for 3D surface reconstruction from two uncalibrated views is an important branch in image-based modeling, which only needs several images with different view-points. We study some techniques in this field, including stereo matching, camera self-calibration, feature points' 3D modeling and texture mapping, etc.
Stereo matching is an important task for the study of computer vision. A stereo matching algorithm based on corner detection is studied. First, the smallest univalue segment assimilating nucleus(SUSAN) approach is used to detect whether the pixels on the edges in the left and right images are corners, and the correspondence relationship by the retained corners is established between the corresponding corners of the left and right images according to the the similarity of the features or interactively. Then the fundament matrix is estimated from matched corners by an improved weighted linear algorithm, which is based on the epipolar geometry and the absolute conic theory.
The camera self-calibration and the featrue points' 3D modeling are two key techniques in image-based modeling. Camera self-calibration includes the computation of camera intrinsic parameters and camera external parameters. Based on fundament matrix and semi-calibrated matrix, the camera intrinsic parameters are computed. Then the external parameters are coumputed by essential matrix. Feature points' 3D coordinates are computed through singular value decomposition of projector matrix, then compute projector matrix by triangulation.
A realistic 3D surface model is built by mapping the triangular textures to the 3D structure surface. Triangular textures are acquired by dividing 2D image with Triangulation.
We have developed a 3D surface reconstruction system from uncalibrated views, which is based on above various kinds of improved algorithms. This system is proven to be effective and satisfactory.
未定标图像的三维模型重建技术是IBM中的一个重要分支，是指只需要场景或物体的不同视点序列图像就可以重构出场景或物体的三维几何模型。本文对该技术的研究包括立体匹配、相机自定标、特征点三维重建、纹理映射等内容。
立体匹配是计算机视觉研究的一项重要课题，本文研究了一种基于角点提取的立体匹配算法。首先使用最小同值分割吸收核(SUSAN)方法判断左右图像的边缘点是否为角点。然后根据角点间的特征相似度，建立起左右图像中被保留角点的匹配关系。利用加权归一算法估计基础矩阵的基础上，引入逐次去除异常匹配点，进行迭代计算，对基础矩阵求精。
相机自定标及对应特征点三维重建是三维重建技术中的关键问题。相机自定标包括相机内参数和相机外参数计算。本文根据基础矩阵估计相机焦距的方法，引入半定标矩阵来计算相机内参数；根据本质矩阵来计算相机的外参数。对应特征点的三维重建是根据三角测量的方法计算其投影矩阵，然后用奇异值分解求出特征点的三维齐次坐标。
本文中的纹理映射是将二维图像的纹理贴到三维框架表面，从而提高模型的可视性，达到“照片级”的视觉效果。本文以特征点为节点对二维图像进行三角剖分，然后将每个三角形纹理图像一一对应地贴到重建的三维模型表面。
基于上述研究，本文开发了一个基于未定标图像的三维模型重建系统，包括自动与互动方式相结合的图像角点提取及匹配，基础矩阵的估计及求精，相机的自定标，特征点三维重建和纹理映射等模块，能够得到较好的三维模型重建结果。
The technique of image-based modeling is important content in computer graphics and computer vision. This technique can extract information of construction, reconstruct 3D model from digital images which captured by DC, DV and some other digital equipments.
The technique for 3D surface reconstruction from two uncalibrated views is an important branch in image-based modeling, which only needs several images with different view-points. We study some techniques in this field, including stereo matching, camera self-calibration, feature points' 3D modeling and texture mapping, etc.
Stereo matching is an important task for the study of computer vision. A stereo matching algorithm based on corner detection is studied. First, the smallest univalue segment assimilating nucleus(SUSAN) approach is used to detect whether the pixels on the edges in the left and right images are corners, and the correspondence relationship by the retained corners is established between the corresponding corners of the left and right images according to the the similarity of the features or interactively. Then the fundament matrix is estimated from matched corners by an improved weighted linear algorithm, which is based on the epipolar geometry and the absolute conic theory.
The camera self-calibration and the featrue points' 3D modeling are two key techniques in image-based modeling. Camera self-calibration includes the computation of camera intrinsic parameters and camera external parameters. Based on fundament matrix and semi-calibrated matrix, the camera intrinsic parameters are computed. Then the external parameters are coumputed by essential matrix. Feature points' 3D coordinates are computed through singular value decomposition of projector matrix, then compute projector matrix by triangulation.
A realistic 3D surface model is built by mapping the triangular textures to the 3D structure surface. Triangular textures are acquired by dividing 2D image with Triangulation.
We have developed a 3D surface reconstruction system from uncalibrated views, which is based on above various kinds of improved algorithms. This system is proven to be effective and satisfactory.
引文
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[2] 杨瑞元，邱建雄．基于图像的建模和绘制技术综述．计算机辅助设计与图形学学报，2002．
[3] 刘钢，彭群生，鲍虎军．基于图像建模技术研究综述与展望．计算机辅助设计与图形学学报，2005，17(1)：311-326．
[4] Maxime hhuillier, Long Quan. A Quasi-Dense Approach to SurfaceReconstruction from Uncalibrated Images. IEEE Transactions on pattern analysis and machine intelligence, 2005.
[5] Heyden A. Geometry and Algebra of Multiple Projective Transformations: (PhD thesis). Lund Inst. of Technology, 1995.
[6] Nister D. Reconstruction from Uncalibrated Sequences with aHierarchy of Trifocal Tensors, Proc. Sixth European Conf. Computer Vision, 2000: 649-663.
[7] Pollefeys M, Koch R, Vergauwen M et al. Metric 3D Surface Reconstruction from Uncalibrated Image Sequences, Proc. European Workshop 3D Structure from Multiple Images of Large-Scale Environments, R. Koch and L. Van Gool, eds. , 1998: 139-154.
[8] Faugeras O and Keriven R. Complete Dense Stereovision Using Level Set Methods, Proc. Fifth European Conf. Computer Vision, 1998: 379-393.
[9] Kolmogorov V, Zabih R. Multi-Camera Scene Reconstruction via Graph Cuts, Proc. Seventh European Conf. Computer Vision, 2002.
[10] Kutulakos K N, Seitz S M. A Theory of Shape by Space Carving, Proc. Seventh Int' 1 Conf. Computer Vision, 1999: 307-314.
[11] Seitz S M, Dyer C R. Photo-Realistic Scene Reconstruction by Voxel Coloring, Proc. Conf. Computer Vision and Pattern Recognition, 1997: 1067-1073.
[12] Pollefeys M, Koch R, Van Gool L. Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters, Proc. Sixth Int' 1 Conf. Computer Vision, 1998: 90-95.
[13] Horn B. Height and gradient from shading. International Journal of Computer Vision, 1990: 37-75.
[14] Super B J, Bovik A C. Shape from texture using local spectral moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995: 333-343.
[15] Nayar S K, Nakagawa Y. Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994: 824-831.
[16] Caprile B, Torre V. Using vanishing points for camera calibration. International Journal of Computer Vision, 1990: 127-140.
[17] Cipolla R, Drummond T, Robertson D. Camera calibration from vanishing points in images of architectural scenes. In: Proceedings of British Machine Vision Conference, Nottingham, England, 1999: 382-391.
[18] Horry Y, Anjyo K, Arai K. Tour into the picture: Using a spidery mesh interface to make animation from a single image. Computer Graphics Proceedings, Annual ConferenceSeries, ACM SIGGRAPH, Los Angeles, California, 1997: 225-232.
[19] Criminisi A, Reid I, Zisserman A. Single view metrology. International Journal of Computer Vision, 2000: 123-148.
[20] 李成军，马志刚，汪国平等．交互的极线几何建模方法．计算机辅助设计与图形学学报，2002：845-847．
[21] SLum H, Man M, Szeliski R. Interactive construction of 3D models from panoramic mosaics. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, SantaBarbara, California, 1998: 427-433.
[22] Debevec P, Taylor C J, Malik J. Modeling and rendering architecture from photographs: A hybrid geometry and image based approach. In: Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH, New Orleans, Louisiana, 1996: 11-20.
[23] 刘彦宏，王洪斌，杜威等．基于图像的树类物体的三维重建．计算机学报，2002，25(9)：930-935．
[24] 梅丽，鲍虎军，郑文庭等．基于实拍图像的人脸真实感重建．计算机学报，2000，23(9)：996-1002．
[25] Scharstein D, Szeliski R. A taxonomy and evaluation of dense two frame stereo correspondence algorithms. International Journal of Computer Vision, 2002, 47(1): 7-42.
[26] Martin W N, Aggarwal J K. Volumetric descriptions of objects from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5(2): 150-158.
[27] Chien C H, Aggarwal J K. Identification of 3D objects from multiple silhouettes using quad trees/octrees. Computer Vision, Graphics, and Image Processing, 1986, 36(2/3): 256-273.
[28] Potmesil M. Generating octree models of 3D objects from their silhouettes in a sequence of images. Computer Vision, Graphics, and Image Processing, 1987, 40(1): 1-29.
[29] Szeliski R. Rapid octree construction from image sequences. CVGIP: Image Understanding, 1993, 58(1): 23-32.
[30] 刘钢，王锐，鲍虎军等．一种可见外壳生成算法．软件学报，2002，13(9)：1823-1829．
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