3D-FHNet: Three-Dimensional Fusion Hierarchical Reconstruction Method for Any Number of Views

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Article proposes a three-dimensional fusion hierarchical reconstruction method that utilizes a multi-view feature combination method and a hierarchical prediction strategy to unify the single view and any number of multiple views 3D reconstructions.

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11 p.

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Lu, Qiang; Lu, Yiyang; Xiao, Mingjie; Yuan, Xiaohui & Jia, Wei November 22, 2019.

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Article proposes a three-dimensional fusion hierarchical reconstruction method that utilizes a multi-view feature combination method and a hierarchical prediction strategy to unify the single view and any number of multiple views 3D reconstructions.

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11 p.

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Abstract: The research field of reconstructing 3D models from 2D images is becoming more and more important. Existing methods typically perform single-view reconstruction or multi-view reconstruction utilizing the properties of recurrent neural networks. Due to the self-occlusion of the model and the special nature of the recurrent neural network, these methods have some problems. We propose a novel three-dimensional fusion hierarchical reconstruction method that utilizes a multi-view feature combination method and a hierarchical prediction strategy to unify the single view and any number of multiple views 3D reconstructions. Experiments show that our method can effectively combine features between different views and obtain better reconstruction results than the baseline, especially in the thin parts of the object. Our source code is available at https://github.com/VIM-Lab/3D-FHNet.

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  • IEEE Access, 7, Institute of Electrical and Electronics Engineers, November 22, 2019, pp. 1-11

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  • Publication Title: IEEE Access
  • Volume: 7
  • Page Start: 172902
  • Page End: 17291
  • Peer Reviewed: Yes

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  • November 22, 2019

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  • June 16, 2020, 10:27 a.m.

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  • Dec. 4, 2023, 11:05 a.m.

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Lu, Qiang; Lu, Yiyang; Xiao, Mingjie; Yuan, Xiaohui & Jia, Wei. 3D-FHNet: Three-Dimensional Fusion Hierarchical Reconstruction Method for Any Number of Views, article, November 22, 2019; (https://digital.library.unt.edu/ark:/67531/metadc1703576/: accessed May 26, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.

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