PAT: Point Cloud Analysis with Local Filter Embedding in Transformer
Xi Li1; Siqi Fan1; Yuanyuan Chen,1; Yuliang Liu,2; Shichao Chen,1; Fenghua Zhu,1; Yisheng Lv1
2022-10
Conference Name2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
Conference DateOctober 8-12, 2022
Conference PlaceMacau, China
Abstract

3D point clouds can offer rich geometric content
of information for the scenes. How to learn geometric features
locally and globally has become a growing concern recently.
This paper proposes a novel approach for Point cloud Analysis
based on Transformer network, called PAT, which utilizes
interactive-attention to capture multi-angle features in irregular
point clouds for classification and segmentation. Specifically, the
double-coordinate encoder is designed as local filter operations
by mining neighborhood information from point coordinates
and direction simultaneously in the bi-coordinate system. The
proposed PAT for point cloud analysis can interactively perform
local and global attention across different feature spaces.
Validation was conducted on two public datasets and results
show that the proposed PAT achieves good performance.

Indexed ByEI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory实体人工智能系统感认知
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51639
Collection多模态人工智能系统全国重点实验室_平行智能技术与系统团队
多模态人工智能系统全国重点实验室
Corresponding AuthorXi Li; Fenghua Zhu,; Yisheng Lv
Affiliation1.the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.Huawei Noah’s Ark Lab
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xi Li,Siqi Fan,Yuanyuan Chen,,et al. PAT: Point Cloud Analysis with Local Filter Embedding in Transformer[C],2022.
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