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Incremental 3D reconstruction using Bayesian learning

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Abstract

We present a novel algorithm for 3D reconstruction in this paper, converting incremental 3D reconstruction to an optimization problem by combining two feature-enhancing geometric priors and one photometric consistency constraint under the Bayesian learning framework. Our method first reconstructs an initial 3D model by selecting uniformly distributed key images using a view sphere. Then once a new image is added, we search its correlated reconstructed patches and incrementally update the result model by optimizing the geometric and photometric energy terms. The experimental results illustrate our method is effective for incremental 3D reconstruction and can be further applied for large-scale datasets or to real-time reconstruction.

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Acknowledgements

The work described in this paper was supported by the Natural Science Foundation of China under Grant No. 61272218 and 61021062, the 973 Program of China under Grant No. 2010CB327903, and the Program for New Century Excellent Talents under NCET-11-0232.

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Authors and Affiliations

  1. State Key Laboratory of Software Novel Technology, Nanjing University, No. 163, Xianlin Avenue, Nanjing, Jiangsu, 210023, China

    Ze-Huan Yuan & Tong Lu

  2. Jiangyin Institute of Information Technology of Nanjing University, No. 163, Xianlin Avenue, Nanjing, Jiangsu, 210023, China

    Tong Lu

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  1. Ze-Huan Yuan
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  2. Tong Lu
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Correspondence to Ze-Huan Yuan.

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Yuan, ZH., Lu, T. Incremental 3D reconstruction using Bayesian learning. Appl Intell 39, 761–771 (2013). https://doi.org/10.1007/s10489-012-0410-8

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