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Technology of Graphic & Image
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925-931,937

Improved single-view surface reconstruction based on sparse feature

Liang Chunyang
Tang Hongmei
Xi Jianrui
Liu Xin
School of Electronics & Information Engineering, Hebei University of Technology, Tianjin 300401, China

Abstract

Single-view 3D reconstruction based on deep learning is a research hot spot at present. In order to discover more high-frequency details, SDF-SRN algorithm introduces positional encoding, but neural network is easy to overfit without accurate supervision, and reconstructs uneven surface. To solve the problem, this paper proposed the network model based on sparse feature. The model enabled the network that preferred to overfitting to predict high-frequency residual by residual learning. The feature extraction network extracted sparse features and the global features. Then one hypernetwork took the sparse features as input and generated prediction shallow head. This shallow head predicted low-frequency part of signed distance function. Another hypernetwork took global features as input and generated another shallow head. This shallow head predicted high-frequency residual. It fused two predictions of shallow heads into final signed distance function. Spectrum analysis shows that the design purpose of network is achieved. Compared with other smooth surface reconstruction schemes, the network can achieve smoother surface reconstruction with enough details. It overcomes the overfitting of SDF-SRN. The qualitative and quantitative comparison with other advanced single-view reconstruction approaches show the superiority of the proposed approach.

Foundation Support

河北省自然科学基金资助项目(F2019202387)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0320
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Technology of Graphic & Image
Pages: 925-931,937
Serial Number: 1001-3695(2023)03-047-0925-07

Publish History

[2022-09-14] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

梁春阳, 唐红梅, 席建锐, 等. 基于稀疏特征改进的单视图表面重建 [J]. 计算机应用研究, 2023, 40 (3): 925-931,937. (Liang Chunyang, Tang Hongmei, Xi Jianrui, et al. Improved single-view surface reconstruction based on sparse feature [J]. Application Research of Computers, 2023, 40 (3): 925-931,937. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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