| Autor: |
Ye, Zhichao, Li, Guanglin, Liu, Haomin, Cui, Zhaopeng, Bao, Hujun, Zhang, Guofeng |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Visualization & Computer Graphics; Nov2022, Vol. 28 Issue 11, p3727-3736, 10p |
| Abstrakt: |
Bundle adjustment (BA) is widely used in SLAM and SfM, which are key technologies in Augmented Reality. For real-time SLAM and large-scale SfM, the efficiency of BA is of great importance. This paper proposes CoLi-BA, a novel and efficient BA solver that significantly improves the optimization speed by compact linearization and reordering. Specifically, for each reprojection function, the redundant matrix representation of Jacobian is replaced with a tiny 3D vector, by which the computational complexity, memory storage, and cache missing for Hessian matrix construction and Schur complement are significantly reduced. Besides, we also propose a novel reordering strategy to improve the cache efficiency for Schur complement. Experiments on diverse datasets show that the speed of the proposed CoLi-BA is five times that of Ceres and two times that of g2o without sacrificing accuracy. We further verify the effectiveness by porting CoLi-BA to the open-source SLAM and SfM systems. Even when running the proposed solver in a single thread, the local BA of SLAM only takes about 20ms on a desktop PC, and the reconstruction of SfM with seven thousand photos only takes half an hour. The source code is available on the webpage: https://github.com/zju3dv/CoLi-BA. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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