FF-LOGO：基于特征滤波与局部到全局优化的跨模态点云配准
FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization
AI总结：
针对跨模态点云配准因传感器模态差异导致的难题，提出FF-LOGO框架，通过特征相关滤波与局部-全局两阶段优化，在3DCSR数据集上将召回率从40.59%提升至75.74%。
摘要：
跨模态点云配准因不同传感器之间的模态固有差异而面临重大挑战。我们提出了一种跨模态点云配准框架FF-LOGO：一种结合特征滤波与局部-全局优化的跨模态点云配准方法。跨模态特征相关滤波模块从跨模态点云中提取几何变换不变特征，并通过特征匹配实现点选择。我们还引入了跨模态优化流程，包括局部自适应关键区域聚合模块和全局模态一致性融合优化模块。实验结果表明，我们的两阶段优化显著提升了特征关联与选择模块的配准精度。在3DCSR数据集上，与当前最先进的方法相比，我们的方法召回率从40.59%大幅提升至75.74%。我们的代码将发布于https://github.com/wangmohan17/FFLOGO。
英文摘要：
Cross-modality point cloud registration is confronted with significant challenges due to inherent differences in modalities between different sensors. We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization. The cross-modality feature correlation filtering module extracts geometric transformation-invariant features from cross-modality point clouds and achieves point selection by feature matching. We also introduce a cross-modality optimization process, including a local adaptive key region aggregation module and a global modality consistency fusion optimization module. Experimental results demonstrate that our two-stage optimization significantly improves the registration accuracy of the feature association and selection module. Our method achieves a substantial increase in recall rate compared to the current state-of-the-art methods on the 3DCSR dataset, improving from 40.59% to 75.74%. Our code will be available at https://github.com/wangmohan17/FFLOGO.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
