基于图优化的增量式单目SFM三维重建方法

Incremental monocular SFM 3D reconstruction method based on graph optimization

相机位姿估计作为SFM的关键环节,其估计精度直接影响三维重建结果,而在增量式SFM的迭代过程中,经常出现由于相机位姿估计误差累积而在最终重建结果中产生漂移问题,文章将图优化理论引入增量式SFM相机位姿估计过程中,以重投影误差平方和为代价函数构造图优化模型,对估算的相机姿态和重建的三维点云进行优化。实验表明本文方法可达到SFM重建要求,且漂移问题得到了明显改善,重建结果具有良好的视觉效果。

Camera pose estimation is a key part of SFM(Structure From Motion), and its accuracy direct affects the results of 3D reconstruction. However, in the iterative process of incremental SFM, there often occurs drift due to camera position and pose estimation error and drift in the final reconstruction results. This paper introduces graphbased optimization theory into the pose estimation process of incremental SFM camera. The graph optimization model is constructed by using the sum of the re-projection errors as the cost function to optimize the estimated camera pose and the reconstructed 3D point cloud. Experiments show that the proposed method can meet the requirements of SFM reconstruction, and the reconstruction results have good visual effects.