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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2022, Vol. 16 Issue (1) : 161301    https://doi.org/10.1007/s11704-020-9521-2
RESEARCH ARTICLE
GridNet: efficiently learning deep hierarchical representation for 3D point cloud understanding
Huiqun WANG, Di HUANG(), Yunhong WANG
Laboratory of Intelligent Recognition and Image Processing, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
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Abstract

In this paper, we propose a novel and effective approach, namely GridNet, to hierarchically learn deep representation of 3D point clouds. It incorporates the ability of regular holistic description and fast data processing in a single framework, which is able to abstract powerful features progressively in an efficient way.Moreover, to capture more accurate internal geometry attributes, anchors are inferred within local neighborhoods, in contrast to the fixed or the sampled ones used in existing methods, and the learned features are thus more representative and discriminative to local point distribution. GridNet delivers very competitive results compared with the state of the art methods in both the object classification and segmentation tasks.

Keywords 3D point clouds      deep representations     
Corresponding Author(s): Di HUANG   
Just Accepted Date: 28 June 2020   Issue Date: 11 June 2021
 Cite this article:   
Huiqun WANG,Di HUANG,Yunhong WANG. GridNet: efficiently learning deep hierarchical representation for 3D point cloud understanding[J]. Front. Comput. Sci., 2022, 16(1): 161301.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9521-2
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I1/161301
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