Communication-Efficient Multi-Vehicle Collaborative Semantic Segmentation via Sparse 3D Gaussian Sharing

Collaborative perception is considered a promising approach to address the inherent limitations of single-vehicle systems by sharing data among vehicles, thereby enhancing performance in perception tasks such as bird's-eye view (BEV) semantic segmentation. However, existing methods share the entire dense, scene-level BEV feature, which contains significant redundancy and lacks height information, ultimately leading to unavoidable bandwidth waste and performance degradation. To address these challenges, we present GSCOOP, the first collaborative semantic segmentation framework that leverages sparse, object-centric 3D Gaussians to fundamentally overcome communication bottlenecks. By representing scenes with compact Gaussians that preserve complete spatial information, GSCOOP achieves both high perception accuracy and communication efficiency. To further optimize transmission, we introduce the Priority-Based Gaussian Selection (PGS) module to adaptively select critical Gaussians and a Semantic Gaussian Compression (SGC) module to compress Gaussian attributes with minimal overhead. Extensive experiments on OPV2V and V2X-Seq demonstrate that GSCOOP achieves state-of-the-art performance, even with more than 500× lower communication volume.The code link is https://github.com/SHEVIP/GSCOOP.