点云
保险丝(电气)
特征提取
特征(语言学)
计算机科学
人工智能
匹配(统计)
云计算
计算机视觉
刀(考古)
点(几何)
数据挖掘
模式识别(心理学)
工程类
数学
电气工程
操作系统
哲学
统计
结构工程
语言学
几何学
作者
Ming Yin,Yangyang Zhu,Guofu Yin,Guoqiang Fu,Luofeng Xie
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:19 (8): 8614-8624
被引量:20
标识
DOI:10.1109/tii.2022.3220889
摘要
Optical measurement methods for blade profiles attract lots of interest in industry. Due to the nature of the thin-walled and twisted spatial freeform surfaces of blades, the measurement accuracy would be significantly affected by the accumulated error associated with the geometric accuracy and motion stability of the developed multiview system. To overcome these issues, this article proposes a deep feature interaction network for fine registration of the multiview data. In our network, we design a two-branch structure to integrate a global and a local feature extraction branch to encode point cloud features. Moreover, we propose a feature interaction module to strengthen information association between two point clouds during feature extraction. Next, an attention mechanism is used to fuse matching information between two matching matrices obtained from the global-based and the local-based features. Experimental results demonstrate the feasibility and good practical application prospect of this method.
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