In the last decade, deep learning (DL) has significantly impacted industry and science. Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted toward 3-D data analysis. In particular, 3-D surface reconstruction, i.e., reconstructing a 3-D shape from sparse input, is of great interest to a large variety of application fields. DL-based approaches show promising quantitative and qualitative surface reconstruction performance compared to traditional computer vision and geometric algorithms. This survey provides a comprehensive overview of these DL-based methods for 3-D surface reconstruction. To this end, we will first discuss input data modalities, such as volumetric data, point clouds, and RGB, single-view, multiview, and depth images, along with corresponding acquisition technologies and common benchmark datasets. For practical purposes, we also discuss evaluation metrics enabling us to judge the reconstructive performance of different methods. The main part of the document will introduce a methodological taxonomy ranging from point- and mesh-based techniques to volumetric and implicit neural approaches. Recent research trends, both methodological and for applications, are highlighted, pointing toward future developments.

中文翻译：

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基于深度学习的 3D 表面重建调查


在过去十年中，深度学习 (DL) 对工业和科学产生了重大影响。最初主要是由 2D 图像中的计算机视觉任务推动的，现在重点已转向 3D 数据分析。特别是，3-D 表面重建，即从稀疏输入重建 3-D 形状，引起了许多应用领域的极大兴趣。与传统计算机视觉和几何算法相比，基于深度学习的方法显示出有前景的定量和定性表面重建性能。本次调查全面概述了这些基于深度学习的 3D 表面重建方法。为此，我们将首先讨论输入数据模式，例如体积数据、点云和 RGB、单视图、多视图和深度图像，以及相应的采集技术和通用基准数据集。出于实用目的，我们还讨论了评估指标，使我们能够判断不同方法的重建性能。该文档的主要部分将介绍一种方法分类，范围从基于点和网格的技术到体积和隐式神经方法。重点介绍了方法论和应用方面的最新研究趋势，指出了未来的发展。