利用空间正交约束的相机自标定和三维重建

提出了一种用2幅未标定的图像进行三维重构的算法.利用空间 3 组垂直方向的正交结构与其在图像平面上消失点的对应关系,线性计算出相机的内参数,再利用3组匹配线段计算相机在2个视角的运动参数,最后根据得到的相机参数采用三角测量法计算空间点的三维坐标值.该算法与利用三正交运动的主动标定法相比,把相机的正交运动约束转换成空间正交结构约束,使用简单,适应性强,可直接利用参数未知的手持数码相机拍摄图像进行三维重建.对真实的建筑场景图像进行三维重建实验,重建后的三维模型在新视点生成的图像与所观察的场景一致,重构的2个平面夹角与实际值的测量误差在1 5%~2 6%之内.

Camera Self-Calibration and Three Dimensional Reconstruction Using Spatial Orthogonal Constraints

A novel method for the three dimensional (3D) reconstruction from two uncalibrated images was described. The camera intrinsic parameters can be estimated linearly by using three pairs of corresponding vanishing points from the three mutual orthogonal space directions, then the camera motion parameters between two views can be estimated from three groups of lines that are mutually orthogonal in 3D space. After calculating the camera projection matrices, the coordinates in 3D space can be calculated using triangular measurement . Comparing with the active camera self-calibration method using three mutual orthogonal translations, the camera orthogonal motion constraints are transformed into the orthogonal constraints of the spatial structure in our approach, so that it is effective to be realized with higher adaptability and the 3D models of the scene can be recovered from 2D images taken by an uncalibrated handhold digital camera. This approach has been applied to the real images of architectural scenes, and a 3D model of the building was reconstructed with better performance. The new images generated from the reconstructed 3D model for the new viewpoints are consistent with the perception of the real scene, and the measure error of the plane angle between the reconstructed 3D model and the real scene is within 1.5%-2.6%.