Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception
This work addresses practical deployment bottlenecks for autonomous driving systems, offering an incremental improvement over existing methods.
The paper tackles the problem of long-range cooperative 3D perception for autonomous driving by addressing computational scaling and feature association errors, achieving state-of-the-art performance in 100-150 m settings with competitive costs.
Cooperative 3D perception via Vehicle-to-Everything 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 features 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 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 100-150 m long-range settings, while maintaining highly competitive computation and communication costs.