  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.
