FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization
This addresses the problem of aligning point clouds from different sensors for applications like robotics or autonomous driving, representing a strong specific gain in accuracy.
The paper tackles cross-modality point cloud registration by proposing FF-LOGO, a framework with feature filtering and local-to-global optimization, which improves recall rate from 40.59% to 75.74% on the 3DCSR dataset.
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.