阵列虚拟相机三维重建研究

3D Reconstruction of Array Virtual Camera

针对目标场景三维重建难以兼顾视场范围和空间分辨率的问题,提出了结合相机和旋转双棱镜的成像系统。该系统可在不同双棱镜转角下形成虚拟相机阵列,同时利用导轨移动调整阵列虚拟相机的成像位置,还可在大视场范围内实现分区域的多视角图像采集。结合相机投影模型和双棱镜折射模型,提出了一种利用几何变换关系的阵列虚拟相机内外参数标定方法,建立了基于三角测量原理的分区域三维点云获取方法。在此基础上,提出了多视角图像重叠区域的预先定位算法,结合迭代最近邻点算法实现由粗到精的分区域点云配准融合。实验结果表明,所提成像系统可以实现大范围高分辨率的三维目标重建,且采用由粗到精的点云配准算法,在提升运算效率的同时还能保证三维重建精度。

In this study, an imaging system combining cameras and rotating Risley prisms is proposed to address the difficulty of considering the field of view and spatial resolution in a target scene’s three-dimensional(3D) reconstruction.The imaging system can form a virtual camera array under different Risley prism rotation angles, adjust the array virtual cameras’ imaging position using rail movement, and realize a subregional multiview image acquisition in a large field of view. A calibration approach of the array virtual camera’s internal and external parameters using a geometrical transformation relationship is proposed, and the subregional 3D point cloud acquisition approach based on the triangulation principle is established, employing the camera projection model and the double-prism refraction model. A prelocalization approach for overlapping regions of multiview pictures is presented on this basis, and a coarse-to-precise subregional point cloud alignment fusion is obtained by integrating the iterative closest neighbor algorithm. The experimental results reveal that the proposed imaging system can achieve largescale and high-resolution 3D target reconstruction and can ensure a 3D reconstruction accuracy due to the coarse-to-precise point cloud alignment approach while enhancing processing efficiency.