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

Yaming Wang, Minjie Wang, Wenqing Huang (), Xiaoping Ye and Mingfeng Jiang
Additional contact information
Yaming Wang: Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, 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

Mathematics, 2022, vol. 10, issue 20, 1-17

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.

Keywords: dense non-rigid structure from motion; weighted spatial constraint; layer normalization layers; deep spatial–temporal neural (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3794/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3794/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:20:p:3794-:d:942633

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().