A survey on deep geometry learning:From a representation perspective

Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D 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 3 D 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.