Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions.

中文翻译：

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深度几何学习调查：从表示的角度

研究人员在使用深度学习处理2D图像方面取得了巨大的成功。近年来，3D计算机视觉和几何深度学习获得了越来越多的关注。已经针对不同的应用提出了许多用于3D形状的高级技术。与可以由规则的像素网格统一表示的2D图像不同，3D形状具有各种表示形式，例如深度图像，多视图图像，体素，点云，网格，隐式表面等。在不同应用程序中实现的性能在很大程度上取决于所使用的表示形式，并且没有适合所有应用程序的唯一表示形式。因此，在本次调查中，我们从表示的角度回顾了3D几何深度学习的最新进展，总结了不同表示形式在不同应用中的优缺点。我们还将以这些表示形式介绍现有的数据集，并进一步讨论未来的研究方向。