A Convolutional Neural Network for Nonrigid Structure from Motion

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses o...

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Bibliographic Details
Published inInternational journal of digital multimedia broadcasting Vol. 2022; pp. 1 - 8
Main Authors Wang, Yaming, Peng, Xiangyang, Huang, Wenqing, Wang, Meiliang
Format Journal Article
LanguageEnglish
Published New York Hindawi 28.04.2022
John Wiley & Sons, Inc
Wiley
Subjects
Accuracy
Artificial neural networks
Cameras
Datasets
Mathematical models
Neural networks
Optimization
Reconstruction
Sequences
Three dimensional models
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Summary:In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.
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ISSN:1687-7578
1687-7586
DOI:10.1155/2022/3582037