Technology of Graphic & Image
|
3487-3491

3D reconstruction network based on multi-scale CNN-RNN

Zhang Ji
Zheng Chuanzhe
School of Control & Computer Engineering, North China Electric Power University, Baoding Hebei 071000, China

Abstract

The existing 3D reconstruction algorithms based on depth learning mainly acquire features from a single layer of depth network, and the feature extraction of two-dimensional images is incomplete, resulting in the unsatisfactory effect of 3D reconstruction. In order to improve the accuracy and accuracy of the 3D reconstruction model, make full use of the details of two-dimensional images, and effectively transform it into a 3D network, this paper proposed a single-graph 3D reconstruction network based on multi-scale CNN-RNN. The model network consisted of three parts: 2D encoder, converter and 3D encoder. Based on the Gauss pyramid model, the model constructed a multi-scale network, retained the eigenvalues of different scales of two-dimensional images, and converted them into three-dimensional features by RNN. It trained and tested the model with a common ShapNet data set. Through comparison, it found that the model has better robustness by using multi-scale feature extraction method. Compared with the existing methods, this model has better reconstruction effect in three-dimensional reconstruction of aircraft, cabinet, car, display, lamp, sound, sofa and other models.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.08.0251
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 11
Section: Technology of Graphic & Image
Pages: 3487-3491
Serial Number: 1001-3695(2020)11-058-3487-05

Publish History

[2020-11-05] Printed Article

Cite This Article

张冀, 郑传哲. 基于多尺度CNN-RNN的单图三维重建网络 [J]. 计算机应用研究, 2020, 37 (11): 3487-3491. (Zhang Ji, Zheng Chuanzhe. 3D reconstruction network based on multi-scale CNN-RNN [J]. Application Research of Computers, 2020, 37 (11): 3487-3491. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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