English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cross-modality point cloud registration is confronted with significantchallenges due to inherent differences in modalities between different sensors.We propose a cross-modality point cloud registration framework FF-LOGO: across-modality point cloud registration method with feature filtering andlocal-global optimization. The cross-modality feature correlation filteringmodule extracts geometric transformation-invariant features from cross-modalitypoint clouds and achieves point selection by feature matching. We alsointroduce a cross-modality optimization process, including a local adaptive keyregion aggregation module and a global modality consistency fusion optimizationmodule. Experimental results demonstrate that our two-stage optimizationsignificantly improves the registration accuracy of the feature association andselection module. Our method achieves a substantial increase in recall ratecompared to the current state-of-the-art methods on the 3DCSR dataset,improving from 40.59% to 75.74%. Our code will be available athttps://github.com/wangmohan17/FFLOGO.

FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization