光学精密工程2024，Vol.32Issue(11)：1759-1772,14.DOI:10.37188/OPE.20243211.1759
面向低重叠率点云匹配的多层级过滤网络
Multi-level filter network for low-overlap point cloud registration
摘要
Abstract
Aiming at the problem of matching distortion caused by structural occlusion,field of view con-straints,and stitching errors during point cloud reconstructed,a multi-level filter network(MulFNet)is proposed to achieve single-shot scanning point clouds for low-overlap registration.Firstly,the multi-level features of the point clouds are extracted through the feature pyramid coding network to obtain semantic in-formation at different scales,and the attention module and the location module are embedded to enhance the feature significance;secondly,the multi-level features are filtered based on the multi-scale consistency voting mechanism,outliers are screened out and prominent features of the point clouds are retained to ob-tain the initial correspondence;and finally,the initial corresponding nodes are adaptively grouped based on the geometric relationships,and weighted estimation conversion is performed from local to global to obtain a prediction matrix based on the multi-level filtering.The experimental results show that the MulFNet is better than the popular networks such as FCGF and PREDATOR on the standard 3DMatch.The registra-tion accuracy of the MulFNet on the scanning dataset with an average overlap rate of 10%is 40.9%and 85.4%higher than the ICP and the GeoTransformer,respectively.It is verified that the proposed net-work can effectively solve the problem of low-overlap point cloud matching distortion.关键词
点云匹配/匹配失真/低重叠率/多层级过滤/局部测量Key words
point cloud registration/matching distortion/low-overlap point cloud/multi-level filter/par-tial measurement分类
信息技术与安全科学引用本文复制引用
贺敏琦,刘俐,李尚,吴浩,朱大虎..面向低重叠率点云匹配的多层级过滤网络[J].光学精密工程,2024,32(11):1759-1772,14.基金项目
国家自然科学基金资助项目(No.52375509) （No.52375509）
湖北省重点研发计划资助项目(No.2022BAA067) （No.2022BAA067）
