版权说明 操作指南
首页 > 成果 > 详情

RRGA-Net: Robust Point Cloud Registration Based on Graph Convolutional Attention

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Qian, Jian;Tang, Dewen
通讯作者:
Tang, DW
作者机构:
[Tang, Dewen; Qian, Jian] Univ South China, Sch Mech Engn, Hengyang 421001, Peoples R China.
通讯机构:
[Tang, DW ] U
Univ South China, Sch Mech Engn, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
point cloud registration;deep learning;attention mechanism
期刊:
Sensors
ISSN:
1424-8220
年:
2023
卷:
23
期:
24
页码:
9651-
基金类别:
Conceptualization, J.Q.; methodology, J.Q.; software, J.Q.; validation, J.Q. and D.T.; formal analysis, D.T.; investigation, J.Q. and D.T.; resources, D.T.; data curation, J.Q. and D.T.; writing—original draft preparation, J.Q.; writing—review and editing, J.Q. and D.T.; visualization, J.Q.; supervision, D.T.; project administration, D.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript. This research was supported by the Hunan Provincial Regional Joint Fund (2023JJ50130).
机构署名:
本校为第一且通讯机构
院系归属:
机械工程学院
摘要:
The problem of registering point clouds in scenarios with low overlap is explored in this study. Previous methodologies depended on having a sufficient number of repeatable keypoints to extract correspondences, making them less effective in partially overlapping environments. In this paper, a novel learning network is proposed to optimize correspondences in sparse keypoints. Firstly, a multi-layer channel sampling mechanism is suggested to enhance the information in point clouds, and keypoints were filtered and fused at multi-layer resolutions to form patches through feature weight filtering. ...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com