Open Access

ARTICLE

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

Xiaorui Zhang^1,2,3,*, Feng Xu², Wei Sun^3,4, Yan Jiang², Yi Cao⁵

1 Wuxi Research Institute, Nanjing University of Information Science & Technology, Wuxi, 214100, China
2 Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China
4 School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China
5 Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

* Corresponding Author: Xiaorui Zhang. Email:

Computer Systems Science and Engineering 2023, 45(2), 1695-1709. https://doi.org/10.32604/csse.2023.031506

Received 19 April 2022; Accepted 16 June 2022; Issue published 03 November 2022

Abstract

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices, and the training time of the reconstructed network is too long. In this paper, we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network (GCN) and topology modification. We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology. Meanwhile, a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically. We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process. Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces, but also has better qualitative and quantitative results.

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Keywords

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