Large Scale 3D Reconstruction-Oriented Fast and Robust Bundle Adjustment Algorithm

Bundle Adjustment is an important component in structure from motion, in order to overcome the drawbacks existing in the robustness, accuracy and space time efficiency of the existing algorithms, a fast and robust bundle adjustment（FRBA） algorithm has been proposed. Firstly, in order to avoid the effects of outliers, we use the Cauchy loss function to potentially down-weight outliers for improving accuracy of the proposed method. Secondly, a large-scale bundle adjustment problem can be divided into some small ones by making use of the sparsity between 3D points and the cameras for reducing the requirements of memory. Finally, according to the inherent property of the matrix after its sparse decomposition, we use a fast matrix factorization algorithm to solve the normal equation. The proposed FRBA algorithm was compared with some state-of-the-art methods on the synthetic dataset, BAL dataset and real image datasets respectively. Experimental results show that the proposed FRBA algorithm outperforms the state-of-the-arts on both time efficiency, space efficiency as well as precision.