解决远距离车路协同3D感知的计算与匹配难题，提升自动驾驶感知能力。
Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception
- 通过几何引导查询生成，精准定位远距离小目标。
- 在100-150米长距下实现领先性能，计算与通信开销低。
- 适合高精度远距离协同感知场景，如高速自动驾驶。
通过车联万物（V2X）通信实现协同3D感知是提升自动驾驶感知能力的有前景范式，可扩展感知范围并解决遮挡问题。然而现有方法在远距离部署中面临两大瓶颈：密集鸟瞰图（BEV）表示带来的二次计算复杂度，以及观测与对齐误差下的特征匹配机制脆弱性。为此，本文提出完全稀疏的Long-SCOPE框架，专为远距离协同3D感知设计。核心包含两个新模块：几何引导查询生成模块，用于准确检测远距离微小物体；可学习的上下文感知关联模块，可在严重位置噪声下稳健匹配协同查询。在V2X-Seq和Griffin数据集上的实验表明，Long-SCOPE在100-150米长距场景中达到当前最优表现，同时保持极低的计算与通信开销。
原文摘要 · Abstract (English)
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.
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