What can we learn about the scene structure from three orthogonal vanishing points in images

被引:15
作者
Wang, Guanghui [1 ,2 ]
Tsui, Hung-Tat [3 ]
Wu, Q. M. Jonathan [1 ]
机构
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada
[2] Aviat Univ, Dept Control Engn, Changchun 130022, Jilin, Peoples R China
[3] Chinese Univ Hong Kong, Dept Elect Engn, Shatin, Hong Kong, Peoples R China
基金
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词
Computer vision; 3D reconstruction; Structured scene; Vanishing point; Visual metrology; SINGLE VIEW METROLOGY; CAMERA CALIBRATION; RECONSTRUCTION; CONSTRAINTS;
D O I
10.1016/j.patrec.2008.09.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The problem of 3D Euclidean reconstruction of structured scenes from uncalibrated images based on the property of vanishing points is studied in this paper. Under the assumption of three-parameter-camera model with varying parameters, we prove that under an arbitrary preassigned world coordinate system, the camera projection matrix of an image can be uniquely determined from three mutually orthogonal vanishing points obtained from the image. When multiple images of the object are present, it is proved that the global consistent projection matrices can be recovered if an arbitrary reference point in space is observed across the views. For the scenario with multiple objects, we may reconstruct each object individually by the proposed method, then register and align them together via the technique of visual metrology so as to obtain the 3D structure of the entire scene. Compared with previous stereovision techniques, the proposed method avoids the bottleneck problem of feature matching and is easy to implement, therefore, more accurate, robust and realistic results are expected. Extensive experiments on synthetic and real world images validate the effectiveness of the proposed method. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:192 / 202
页数:11
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