Large-Scale bundle adjustment by parameter vector partition

Authors:
Shanmin Pang;Jianrue Xue;Le Wang;Nanning Zheng
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Year:
2012

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Abstract

We propose an efficient parallel bundle adjustment (BA) algorithm to refine 3D reconstruction of the large-scale structure from motion (SfM) problem, which uses image collections from Internet. Different from the latest BA techniques that improve efficiency by optimizing the reprojection error function with Conjugate Gradient (CG) methods, we employ the parameter vector partition strategy. More specifically, we partition the whole BA parameter vector into a set of individual sub-vectors via normalized cut (Ncut). Correspondingly, the solution of the BA problem can be obtained by minimizing subproblems on these sub-vector spaces. Our approach is approximately parallel, and there is no need to solve the large-scale linear equation of the BA problem. Experiments carried out on a low-end computer with 4GB RAM demonstrate the efficiency and accuracy of the proposed algorithm.