Open Access

ARTICLE

Aggregate Point Cloud Geometric Features for Processing

Yinghao Li^1,2,3, Renbo Xia^1,2,*, Jibin Zhao^1,2,*, Yueling Chen^1,2, Liming Tao^1,2,3, Hangbo Zou^1,2,3, Tao Zhang^1,2,3

1 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2 Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China
3 University of Chinese Academy of Sciences, Beijing, 100049, China

* Corresponding Authors: Renbo Xia. Email: ; Jibin Zhao. Email:

(This article belongs to the Special Issue: Recent Advances in Virtual Reality)

Computer Modeling in Engineering & Sciences 2023, 136(1), 555-571. https://doi.org/10.32604/cmes.2023.024470

Received 31 May 2022; Accepted 20 September 2022; Issue published 05 January 2023

Abstract

As 3D acquisition technology develops and 3D sensors become increasingly affordable, large quantities of 3D point cloud data are emerging. How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved. The point cloud geometric information is hidden in disordered, unstructured points, making point cloud analysis a very challenging problem. To address this problem, we propose a novel network framework, called Tree Graph Network (TGNet), which can sample, group, and aggregate local geometric features. Specifically, we construct a Tree Graph by explicit rules, which consists of curves extending in all directions in point cloud feature space, and then aggregate the features of the graph through a cross-attention mechanism. In this way, we incorporate more point cloud geometric structure information into the representation of local geometric features, which makes our network perform better. Our model performs well on several basic point clouds processing tasks such as classification, segmentation, and normal estimation, demonstrating the effectiveness and superiority of our network. Furthermore, we provide ablation experiments and visualizations to better understand our network.

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Keywords

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