Keywords
3D shape representation, geometry learning;neural networks, computer graphics
Abstract
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 representa-tion 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.
Publisher
Tsinghua University Press
Recommended Citation
Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang et al. A survey on deep geometry learning: From a representation perspective. Computational Visual Media 2020, 6(2): 113-133.
Included in
Computational Engineering Commons, Computer-Aided Engineering and Design Commons, Graphics and Human Computer Interfaces Commons, Software Engineering Commons