深度三维重建：方法、数据和挑战

刘彩霞; 孔德慧; 王少帆; 王志勇; 李敬华; 尹宝才

doi:10.1631/FITEE.2000068

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深度三维重建：方法、数据和挑战

常规文章 | Updated：2022-05-19

- 深度三维重建：方法、数据和挑战
  Enhanced Publication
- Deep 3D reconstruction: methods, data, and challenges
- 信息与电子工程前沿（英文） 2021年22卷第5期页码：652-672
- Affiliations：
  
  Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  Multimedia Laboratory, School of Computer Science, University of Sydney, Sydney NSW 2006, Australia
- Author bio：
  
  [ "Caixia LIU, E-mail: lcxxib@emails.bjut.edu.cn" ]
  Shaofan WANG, E-mail: wangshaofan@bjut.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61772049, 61632006, 61876012, U19B2039, and 61906011) and the Beijing Natural Science Foundation of China (No. 4202003)
- DOI：10.1631/FITEE.2000068
  中图分类号： TP391
- 纸质出版日期：2021-05，
  
  收稿日期：2020-02-11，
  
  修回日期：2020-12-29，
- Accepted：
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刘彩霞, 孔德慧, 王少帆, 等. 深度三维重建：方法、数据和挑战[J]. 信息与电子工程前沿（英文）, 2021,22(5):652-672.

CAIXIA LIU, DEHUI KONG, SHAOFAN WANG, et al. Deep 3D reconstruction: methods, data, and challenges. [J]. Frontiers of information technology & electronic engineering, 2021, 22(5): 652-672.
刘彩霞, 孔德慧, 王少帆, 等. 深度三维重建：方法、数据和挑战[J]. 信息与电子工程前沿（英文）, 2021,22(5):652-672. DOI： 10.1631/FITEE.2000068.

CAIXIA LIU, DEHUI KONG, SHAOFAN WANG, et al. Deep 3D reconstruction: methods, data, and challenges. [J]. Frontiers of information technology & electronic engineering, 2021, 22(5): 652-672. DOI： 10.1631/FITEE.2000068.

摘要

三维形状重建是计算机视觉、计算机图形学、模式识别和虚拟现实等领域的重要研究课题。现有三维重建方法通常存在两个瓶颈：（1）它们涉及多个人工设计阶段，导致累积误差，且难以自动学习三维形状的语义特征；（2）它们严重依赖图像内容和质量，以及精确校准的摄像机。因此，这些方法的重建精度难以提高。基于深度学习的三维重建方法通过利用深度网络自动学习低质量图像中的三维形状语义特征，克服了这两个瓶颈。然而，这些方法具有多种体系框架，但是至今未有文献对它们作深入分析和比较。本文对基于深度学习的三维重建方法进行全面综述。首先，基于不同深度学习模型框架，将基于深度学习的三维重建方法分为4类：递归神经网络、深自编码器、生成对抗网络和卷积神经网络，并对相应方法作详细分析。其次，详细介绍上述方法常用的4个代表性数据库。再次，对基于深度学习的三维重建方法进行综合比较，包括不同方法在同一数据库、同一方法在不同数据库以及同一方法对于不同视角个数输入的结果比较。最后，讨论了基于深度学习的三维重建方法的发展趋势。

Abstract

Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision

computer graphics

pattern recognition

and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors

but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images

as well as precisely calibrated cameras. As a result

it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However

while these methods have various architectures

in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First

based on different deep learning model architectures

we divide 3D reconstruction methods based on deep learning into four types

recurrent neural network

deep autoencoder

generative adversarial network

and convolutional neural network based methods

and analyze the corresponding methodologies carefully. Second

we investigate four representative databases that are commonly used by the above methods in detail. Third

we give a comprehensive comparison of 3D reconstruction methods based on deep learning

which consists of the results of different methods with respect to the same database

the results of each method with respect to different databases

and the robustness of each method with respect to the number of views. Finally

we discuss future development of 3D reconstruction methods based on deep learning.

关键词

深度学习模型三维重建循环神经网络深度自编码器生成对抗网络卷积神经网络

Keywords

Deep learning modelsThree-dimensional reconstructionRecurrent neural networkDeep autoencoderGenerative adversarial networkConvolutional neural network

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