Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

Cooperative 3D perception via Vehicle-to-Everything (V2X) communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution.However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks:the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors.To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception.Our method introduces two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects,and a learnable Context-Aware Association module that robustly matches cooperative queries even despite severe positional noise.Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging long-range settings, while maintaining superior computational efficiency and a highly competitive transmission cost.