A Preconditioned Landweber iteration-based Bundle adjustment for large-scale 3D reconstruction

Abstract

Bundle Adjustment (BA) plays a crucial role in 3D reconstruction methods and Structure from Motion (SFM) algorithms, as it involves nonlinear minimization of reprojection error. The efficiency and accuracy of BA solutions are of paramount importance for achieving high-quality 3D reconstructions. The solution of the normal equations derived from nonlinear minimization is typically the most time-consuming step in BA and directly impacts the reconstruction accuracy. In this paper, we propose a Preconditioned Landweber Iteration solver for the normal equations. This solver utilizes a preconditioner and a corresponding relaxation strategy to improve the conditioning of the normal equations, leading to improved convergence speed and accuracy of the iteration. Compared to traditional direct and iterative algorithms, our approach significantly enhances the accuracy and solution time of BA. Experimental results demonstrate that our proposed solver outperforms the Schur complement solver in terms of both speed and accuracy when solving the normal equations. This improvement contributes to further acceleration and accuracy in large-scale 3D reconstruction, particularly for challenging large-scale intensive scenarios.