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Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive Points

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Abstract

Bundle adjustment (BA) is a crucial but time consuming step in 3D reconstruction. In this paper, we intend to tackle a special class of BA problems where the reconstructed 3D points are much more numerous than the camera parameters, called Massive-Points BA (MPBA) problems. This is often the case when high-resolution images are used. We present a design and implementation of a new bundle adjustment algorithm for efficiently solving the MPBA problems. The use of hardware parallelism, the multi-core CPUs as well as GPUs, is explored. By careful memory-usage design, the graphic-memory limitation is effectively alleviated. Several modern acceleration strategies for bundle adjustment, such as the mixed-precision arithmetics, the embedded point iteration, and the preconditioned conjugate gradients, are explored and compared. By using several high-resolution image datasets, we generate a variety of MPBA problems, with which the performance of five bundle adjustment algorithms are evaluated. The experimental results show that our algorithm is up to 40 times faster than classical Sparse Bundle Adjustment, while maintaining comparable precision.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Xin Liu, Wei Gao & Zhan-Yi Hu

Authors
  1. Xin Liu
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  2. Wei Gao
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  3. Zhan-Yi Hu
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Corresponding author

Correspondence to Xin Liu.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 60835003 and the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA06030300.

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Liu, X., Gao, W. & Hu, ZY. Hybrid Parallel Bundle Adjustment for 3D Scene Reconstruction with Massive Points. J. Comput. Sci. Technol. 27, 1269–1280 (2012). https://doi.org/10.1007/s11390-012-1303-3

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  • DOI: https://doi.org/10.1007/s11390-012-1303-3

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