Abstract
Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion; namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency.
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Acknowledgements
The authors gratefully acknowledge the support of the National Key Research and Development Plan (Grant No. 2016YFC0800100), the National Natural Science Foundation (Grant Nos.: 61972128, 61802103, 61877016, 61602146 and 61673157), China Postdoctoral Science Foundation (Grant No.: 2018M632522), Fundamental Research Funds for the Central Universities (Grant Nos.: PA2019GDPK0071, JZ2018HGBH0280 and PA2018GDQT0014), and Key Research and Development Program in Anhui Province (Grant No.: 1804a09020036).
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Cao, M., Zheng, L., Jia, W. et al. Fast incremental structure from motion based on parallel bundle adjustment. J Real-Time Image Proc 18, 379–392 (2021). https://doi.org/10.1007/s11554-020-00970-3
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DOI: https://doi.org/10.1007/s11554-020-00970-3