Long-SCOPE：全稀疏长距离协同3D感知
Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception
面向车路协同长距离感知，用几何引导的查询生成替代稠密鸟瞰图，并用可学习的上下文感知关联模块在位置噪声下鲁棒匹配协同查询，可直接迁移到多智能体/多视角融合中的稀疏查询关联与远距离小目标检测。
原文依据 a Geometry-guided Query Generation module to accurately detect small, distant objects
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论通过车联万物通信实现的协同3D感知是增强自动驾驶的一种有前景的范式，能够扩展感知范围并解决遮挡问题。然而，现有方法在远距离的实际部署受到两个关键瓶颈的阻碍：稠密鸟瞰图表示的二次计算复杂度，以及在显著观测与对齐误差下特征关联机制的脆弱性。为克服这些局限，我们提出 Long-SCOPE，一个面向鲁棒长距离协同3D感知的全稀疏框架。该方法包含两个新颖组件：几何引导的查询生成模块，用于准确检测远处的小目标；以及可学习的上下文感知关联模块，即使在严重的位置噪声下也能鲁棒地匹配协同查询。在 V2X-Seq 和 Griffin 数据集上的实验验证，Long-SCOPE 取得了最优性能，尤其是在具有挑战性的100-150米长距离场景中，同时保持了极具竞争力的计算与通信开销。
英文原文摘要 1105 字符
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 100-150m long-range settings, while maintaining highly competitive computation and communication costs.