ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 213-218, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-213-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT OF DUNHUANG WALL PAINTING IMAGES
K. Hu1, X. Huang2, and H. You1 1Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, No. 19 North 4th Ring Road West, 100190, Beijing, China
2Wuhan Engineering Science and Technology Institute, Jiangda Road 30, 430019, Wuhan, Hubei, China
Keywords: Calculus of variations, Conjugate gradient bundle adjustment (CGBA), Dunhuang wall painting images, Improved incomplete Cholesky factorization, Space complexity, Time complexity Abstract. Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters and quasi-planar constraints, the structure of coefficient matrix of the reduced normal equation is banded-bordered, making the solving process of bundle adjustment complex. In this paper, Conjugate Gradient Bundle Adjustment (CGBA) method is deduced by calculus of variations. A preconditioning method based on improved incomplete Cholesky factorization is adopt to reduce the condition number of coefficient matrix, as well as to accelerate the iteration rate of CGBA. Both theoretical analysis and experimental results comparison with conventional method indicate that, the proposed method can effectively conquer the ill-conditioned problem of normal equation and improve the calculation efficiency of bundle adjustment with additional parameters considerably, while maintaining the actual accuracy.
Conference paper (PDF, 749 KB)


Citation: Hu, K., Huang, X., and You, H.: IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT OF DUNHUANG WALL PAINTING IMAGES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 213-218, https://doi.org/10.5194/isprs-annals-IV-2-W4-213-2017, 2017.

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