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Review Article | Open Access

A survey on deep geometry learning: From a representation perspective

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
School of Computer Science and Informatics, Cardiff University, Wales, UK.
School of Engineering and Computer Science, Victoria University of Wellington, New Zealand.
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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.

Keywords

3D shape representationgeometry learning;neural networkscomputer graphics

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Computational Visual Media
Volume 6 Issue 2,
June 2020
Pages 113-133
DOI: 10.1007/s41095-020-0174-8
Cite this article:
Xiao Y-P, Lai Y-K, Zhang F-L, et al. A survey on deep geometry learning: From a representation perspective. Computational Visual Media, 2020, 6(2): 113-133. https://doi.org/10.1007/s41095-020-0174-8

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Received: 16 February 2020
Revised: 16 February 2020
Accepted: 17 April 2020
Published: 10 June 2020
© The Author(s) 2020

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