基于深度学习的单幅图像三维重建算法

doi:10.16039/j.cnki.cn22-1249.2020.01.013

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摘要

图像三维重建在逆向工程、人工智能等领域广泛应用基于深度学习利用单幅图像重构出三维模型，已经成为当前研究的热点。文章首先综述单幅图像三维重建的研究现状，重点研究基于体素表达的3D-R2N2、基于点云表达的PSGN、基于单片网格表达的Pixel2Mesh和基于多片三角形网格表达的AtlasNet四种算法，通过实验对比研究，来分析解决不同任务与输出模型不同表达方式的选择问题。

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	朱莉
	陈辉

关键词: 深度学习单幅图像三维重建

Abstract:

Image 3D reconstruction is widely used in reverse engineering, artificial intelligence and other fields. It is a hot research topic based on deep learning to reconstruct a 3D model using a single image. The article first reviews the research status of 3D reconstruction of single image, focusing on 3D-R2N2 based on voxel expression, PSGN based on point cloud expression, Pixel2Mesh based on monolithic grid expression and AtlasNet based on multi-piece triangle mesh expression. The algorithm analyzes the problem of different expressions of different tasks and output models through experimental comparative research.

Key words: deep learning single image 3D reconstruction

出版日期: 2020-01-25 发布日期: 2020-01-25 整期出版日期: 2020-01-25

TP391.4

引用本文:

朱莉, 陈辉. 基于深度学习的单幅图像三维重建算法 [J]. 吉林化工学院学报, 2020, 37(1): 58-62.
ZHU Li, CHEN Hui. Single Image 3D Reconstruction Algorithm Based on Deep Learning . Journal of Jilin Institute of Chemical Technology, 2020, 37(1): 58-62.

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https://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2020.01.013 或 https://xuebao.jlict.edu.cn/CN/Y2020/V37/I1/58

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