24 October 2017 Hard constrained sparse bundle adjustment of multi-camera with block matrix
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Proceedings Volume 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing; 104581N (2017) https://doi.org/10.1117/12.2285619
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Because of the ability to optimize the 3D points and viewing parameters jointly and simultaneously, Sparse Bundle Adjustment (SBA) is an essential procedure and usually used as the last step of Structure from Motion (SFM). Recent development of SBA is incline to research on combination of the numeric method with matrix compression technique for more efficient and less memory consuming, and of prior information with SBA for the high accuracy. In this paper, a new hard constrained SBA method for multi-camera is presented. This method takes the prior information of 3D model or multi-camera into account as a hard constraint, and its solution is accomplished by the Lagrange multiplier method and Schur complement combined and with block matrix. The contribution of this work is that it provides a solution integrate constraint and multi-camera SBA, which is desired in the SFM problem and photogrammetry area. Another noticeable aspect is that obvious less time consuming with block matrix based than without, and the accuracy is maintained.
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ZhongChen Shi, JunFeng Sun, Yang Shang, XiaoHu Zhang, "Hard constrained sparse bundle adjustment of multi-camera with block matrix", Proc. SPIE 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing, 104581N (24 October 2017); doi: 10.1117/12.2285619; https://doi.org/10.1117/12.2285619
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