The field of single-view 3D shape reconstruction and generation using deep learning techniques has seen rapid growth in the past five years. As the field is reaching a stage of maturity, a plethora of methods has been continuously proposed with the aim of pushing the state of research further. This article focuses on surveying the literature by classifying these methods according to the shape representation they use as an output. Specifically, it covers each method’s main contributions, degree of supervision, training paradigm, and its relation to the whole body of literature. Additionally, this survey discusses common 3D datasets, loss functions, and evaluation metrics used in the field. Finally, it provides a thorough analysis and reflections on the current state of research and provides a summary of the open problems and possible future directions. This work is an effort to introduce the field of data-driven single-view 3D reconstruction to interested researchers while being comprehensive enough to act as a reference to those who already do research in the field.

在过去的五年中，使用深度学习技术的单视图3D形状重建和生成领域迅速发展。随着该领域的成熟，已经不断提出了许多方法，以进一步推动研究的发展。本文重点研究文献，方法是根据它们用作输出的形状表示形式对这些方法进行分类。具体来说，它涵盖了每种方法的主要贡献，监督程度，培训范式及其与整个文献的关系。此外，本次调查讨论了现场使用的常见3D数据集，损失函数和评估指标。最后，它提供了对当前研究状态的透彻分析和反思，并对未解决的问题和可能的未来方向进行了总结。这项工作是为了向感兴趣的研究人员介绍数据驱动的单视图3D重建领域，同时又要足够全面，以作为已在该领域进行研究的人员的参考。