目前常见的多角度融合三维重构算法主要包括迭代最近点(ICP)算法与运动恢复结构(SFM)算法。针对两种算法的不足, 结合双目相机特点, 提出一种将ICP与SFM相结合的双目立体视觉多角度融合三维重构算法。首先, 采用SFM算法通过双目相机在目标周围拍摄n组图像对, 在每组双目图像对中手动选取目标的初始匹配特征点, 计算其三维坐标, 生成n组三维点云; 接着, 通过ICP算法计算并优化n组三维点云之间的旋转矩阵与平移向量完成融合, 通过Delaunay三角剖分与纹理映射恢复出目标的立体几何形状。实验结果表明：本文算法根据双目相机特点, 集合了ICP算法与SFM算法的优点, 并最大限度地克服了两种算法的不足, 重构效果良好。

Currently, the commonly used multi-angle fusion three-dimensional (3D) reconstruction algorithm mainly includes the iterative closest point (ICP) algorithm and the structure from motion (SFM) algorithm. Aiming at the shortcomings of the above algorithms, we propose a multi-angle fusion 3D reconstruction algorithm with binocular stereo based on ICP and SFM. Firstly, the n groups of photos are taken around the target with binocular cameras by using the SFM algorithm. Then, we manually select the matching feature points of target in each group of binocular images, and calculate the 3D coordinate of matching feature points to generate the n groups of 3D point cloud. Subsequently, the rotation matrix and translation vector within the n groups of 3D point cloud are calculated and optimized by ICP algorithm. Finally, the n groups of 3D point are fused, and the 3D geometry of target is recovered by Delaunay triangle. The experimental results show that, the proposed algorithm takes advantages of the binocular cameras, overcomes the disadvantages of ICP and SFM algorithm, and has good vision effect for 3D reconstruction of target.