论文标题
一项关于深度几何学习的调查：从表示的角度来看
A Survey on Deep Geometry Learning: From a Representation Perspective
论文作者
论文摘要
现在，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计算机视觉和几何学深度学习引起了越来越多的关注。已经为不同的应用提出了许多针对3D形状的高级技术。与2D图像不同，可以通过像素的常规网格统一表示，3D形状具有各种表示形式，例如深度和多视图图像，基于Voxel的表示，基于Voxel的表示，基于点的表示，基于网格的表示，隐含的表面表示等。但是，不同应用程序的性能在很大程度上依赖于使用的代表，并且没有唯一的代表来实现各种应用程序。因此，在这项调查中，我们从表示形式的角度回顾了3D几何学深度学习的最新发展，总结了不同应用中不同表示的优势和缺点。我们还介绍了这些表示形式中的现有数据集，并进一步讨论了未来的研究方向。
Researchers have now achieved great success on dealing with 2D images using deep learning. In recent years, 3D computer vision and Geometry Deep Learning gain more and more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by regular grids of pixels, 3D shapes have various representations, such as depth and multi-view images, voxel-based representation, point-based representation, mesh-based representation, implicit surface representation, etc. However, the performance for 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 development in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations in different applications. We also present existing datasets in these representations and further discuss future research directions.
