Aligning Sparse Messages for Pose-Robust Collaborative Perception

Collaborative perception improves detection by sharing observations across vehicles and roadside units, but its benefits are limited by communication cost and pose noise. Under a limited communication budget, features useful for detection may still be difficult to match across agents. We propose SAComm, a collaborative perception framework that jointly designs sparse patch messages and receiver-side alignment and fusion. Each sender transmits a small number of compressed local feature patches, preserving local structure for both detection and matching. The receiver matches these patches against ego features and estimates a shared translation and yaw correction for each sender while accounting for matching uncertainty and geometric consistency. An adaptive gate controls how much correction is applied to the entire message, while pose reliability guides local fusion. Thus, the same sparse messages support both alignment and collaborative detection. Experiments on OPV2V, V2XSet, and DAIR-V2X-C show that SAComm maintains strong detection performance under pose noise at the lowest communication rates among the evaluated feature-level methods. On OPV2V, it achieves 86.98% BEV AP70 under strong pose noise at only 0.393 Mbps per sender.