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Deep Spatial-Temporal Neural Network for Dense Non-Rigid Structure from Motion

Author

Listed:
  • Yaming Wang

    (Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, China
    Key Laboratory of Digital Design and Intelligent Manufacture in Culture & Creativity Product of Zhejiang Province, Lishui University, Lishui 323000, China)

  • Minjie Wang

    (Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, China)

  • Wenqing Huang

    (Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, China)

  • Xiaoping Ye

    (Key Laboratory of Digital Design and Intelligent Manufacture in Culture & Creativity Product of Zhejiang Province, Lishui University, Lishui 323000, China)

  • Mingfeng Jiang

    (Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, China)

Abstract

Dense non-rigid structure from motion (NRSfM) has long been a challenge in computer vision because of the vast number of feature points. As neural networks develop rapidly, a novel solution is emerging. However, existing methods ignore the significance of spatial–temporal data and the strong capacity of neural networks for learning. This study proposes a deep spatial–temporal NRSfM framework (DST-NRSfM) and introduces a weighted spatial constraint to further optimize the 3D reconstruction results. Layer normalization layers are applied in dense NRSfM tasks to stop gradient disappearance and hasten neural network convergence. Our DST-NRSfM framework outperforms both classical approaches and recent advancements. It achieves state-of-the-art performance across commonly used synthetic and real benchmark datasets.

Suggested Citation

  • Yaming Wang & Minjie Wang & Wenqing Huang & Xiaoping Ye & Mingfeng Jiang, 2022. "Deep Spatial-Temporal Neural Network for Dense Non-Rigid Structure from Motion," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3794-:d:942633
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