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基于场景几何约束未标定两视图的三维模型重建
引用本文:杨敏,沈春林.基于场景几何约束未标定两视图的三维模型重建[J].中国图象图形学报,2003,8(8):872-876.
作者姓名:杨敏  沈春林
作者单位:南京航空航天大学自动化学院 南京210016 (杨敏),南京航空航天大学自动化学院 南京210016(沈春林)
基金项目:国防科工委预研基金 (B0 12 2 -0 3 5 )
摘    要:提出了一种从两幅未标定图象重建场景三维模型的方法 .这种方法充分利用了人造结构场景中大量存在的平行性和正交性几何约束 ,即利用每幅视图中三组互相垂直的平行线 ,计算出 3个影灭点 ,从而对每幅视图进行标定 .对两幅未标定图象 ,从基本矩阵只能得到射影重构 ,如果每幅图象都已标定 ,则可将基本矩阵转化为本质矩阵 .三维重构过程有两个步骤 :先是恢复相机的位置和运动 ;后是用三角测量法计算出点的三维坐标 .对多平面组成的场景进行三维重构实验 ,所得三维模型产生新的视点图象 ,与所观察的场景一致 ,重构的两个平面夹角与实际值相近 ,实验结果表明 ,该算法是行之有效的

关 键 词:计算机图象处理(520·6040)  影灭点  相机标定  基本矩阵  本质矩阵  三角测量法  三维重构
文章编号:1006-8961(2003)08-0872-05
修稿时间:2002年12月9日

Uncalibrated Two-views 3D Reconstruction Based on Geometric Constraints in Scene
YANG Min and SHEN Chun-lin.Uncalibrated Two-views 3D Reconstruction Based on Geometric Constraints in Scene[J].Journal of Image and Graphics,2003,8(8):872-876.
Authors:YANG Min and SHEN Chun-lin
Abstract:In this paper, the methods for the uncalibrated two-views 3D reconstruction is proposed. The methods employ geometric constraints available from geometric relationships that are plentiful in manmade structure-such as parallelism and orthogonality of lines and planes, these constraints lead to simple method to calibrate the intrinsic parameters of the camera. This is done by determining the vanishing points associated with parallel lines in the world, under the assumption of zero skew and known aspect ratio, three mutually orthogonal directions are exploited to give the camera calibration matrix . It is possible to obtain only a projective reconstruction from the fundamental matrix. If each image is calibrated, it be able to convert from the uncalibrated fundamental matrix to the essential matrix. A Euclidean reconstruction would be preferable. Once the essential matrix is recovered, if the first camera is assumed to be at origin of the coordinate system, then it is a simple matter to calculate the rotation and translation of the second camera relative to the first. After the camera intrinsic and extrinsic parameters had been estimated, camera projection matrices may be recovered and used to estimate the structure. The 3D reconstruction process has two stages: the first to recover the camera positions and motions, the second step involves triangulation to recover the 3D points. The validity of the proposed algorithm is confirmed by experiment for a number of multi-plannar scenes. The reconstructed scene is modeled. New images are generated of the model for new view points. The geometry agrees with our perception of scene. The angle between the two reconstructed planes looks just like a right angle.
Keywords:Computer image processing  Vanishing points  Camera calibration  Fundamental matrix  Essential matrix  Triangulation  3D reconstruction
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