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Multi-view 3D reconstruction by random-search and propagation with view-dependent patch maps

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Abstract

This paper proposes an efficient multi-view 3D reconstruction method based on randomization and propagation scheme. Our method progressively refines a 3D model of a given scene by randomly perturbing the initial guess of 3D points and propagating photo-consistent ones to their neighbors. While finding local optima is an ordinary method for better photo-consistency, our randomization and propagation takes lucky matchings to spread better points replacing old ones for reducing the computational complexity. Experiments show favorable efficiency of the proposed method accompanied by competitive accuracy with the state-of-the-art methods.

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Notes

  1. http://grail.cs.washington.edu/rome/rome/index_5.html

  2. http://vision.middlebury.edu/mview/eval/

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Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center))

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  1. Department of Computer Science, Yonsei University, Seoul, Korea

    Youngjung Uh & Hyeran Byun

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  1. Youngjung Uh
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  2. Hyeran Byun
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Correspondence to Hyeran Byun.

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Uh, Y., Byun, H. Multi-view 3D reconstruction by random-search and propagation with view-dependent patch maps. Multimed Tools Appl 75, 16597–16614 (2016). https://doi.org/10.1007/s11042-016-3621-x

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