RC-GeoCP: Geometric Consensus for Radar-Camera Collaborative Perception

Collaborative perception (CP) improves scene understanding through multi-agent information sharing, yet LiDAR-centric systems remain costly and vulnerable in adverse weather. Camera--4D radar offers a practical alternative, but their synergy is still underexplored in CP. We introduce RC-GeoCP, which promotes low-cost, weather-resilient, and geometrically stable radar from an ego-level auxiliary cue to a cross-agent collaboration anchor. To resolve misalignment caused by depth ambiguity and spatial dispersion across agents, RC-GeoCP establishes an ego-normalized geometric consensus: the same radar-derived reliability prior is reused to ground local BEV features, select complementary messages, and weight received evidence. Specifically, Geometric Structure Rectification (GSR) aligns visual semantics with geometry derived from radar to generate spatially grounded, geometry-consistent representations. Uncertainty-Aware Communication (UAC) then serves as an information filter that inherits rectified features from GSR, leveraging inter agent disagreement to steer selective communication toward the most informative regions. Finally, the Consensus-Driven Assembler (CDA) aggregates multi-agent information via ego-normalized geometric anchors to form a spatially coherent representation. We establish a unified radar-camera CP evaluation protocol on V2X-Radar and V2X-R, demonstrating a strong accuracy--communication trade-off. Code will be released soon.