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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.


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A survey on deep geometry learning: From a representation perspective

Show Author's information Yun-Peng Xiao1Yu-Kun Lai2Fang-Lue Zhang3Chunpeng Li1Lin Gao1( )
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.

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 representation, geometry learning;neural networks, computer graphics
Received: 16 February 2020 Revised: 16 February 2020 Accepted: 17 April 2020 Published: 10 June 2020 Issue date: June 2020
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Publication history

Received: 16 February 2020
Revised: 16 February 2020
Accepted: 17 April 2020
Published: 10 June 2020
Issue date: June 2020

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© The Author(s) 2020

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61828204, 61872440), Beijing Municipal Natural Science Foundation (L182016), Youth Innovation Promotion Association CAS, CCF-Tencent Open Fund, Royal Society-Newton Advanced Fellowship (NAF\R2\192151), and the Royal Society (IES\R1\180126).

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