CoLC：结合激光雷达补全的通信高效协同感知
CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion
在带宽受限下用前景感知点采样加激光雷达补全重建稠密 pillar 再做早期融合，为多智能体协同感知提供了一条兼顾互补性与通信开销的路线，可直接迁移到车路协同与多车 BEV 感知系统。
原文依据 we propose a communication-efficient early collaborative perception framework that incorporates LiDAR completion to restore scene completeness under sparse transmission
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论协同感知使自动驾驶智能体能够共享互补信息并克服感知局限。早期融合具有更强的感知互补性，且对模型异构性天然鲁棒，但其高昂的通信开销限制了实际部署，因此现有工作大多倾向于中间融合或晚期融合。为解决这一问题，我们提出了一种通信高效的早期协同感知框架，该框架引入激光雷达补全以在稀疏传输条件下恢复场景完整性，称为 CoLC。具体而言，CoLC 集成了三项互补设计。首先，每个邻居智能体采用前景感知点采样（FAPS），在带宽约束下选择性地传输保留关键结构与上下文线索的信息性点。随后，自车智能体采用补全增强早期融合（CEEF），从接收到的稀疏输入中重建稠密 pillar，并将其与自身观测自适应融合，从而恢复空间完整性。最后，稠密引导双重对齐（DGDA）策略在训练期间强制增强 pillar 与稠密 pillar 之间的语义与几何一致性，确保一致且鲁棒的特征学习。在仿真与真实数据集上的实验表明，CoLC 实现了更优的感知—通信权衡，并在异构模型设置下保持鲁棒。
英文原文摘要 1440 字符
Collaborative perception empowers autonomous agents to share complementary information and overcome perception limitations. While early fusion offers more perceptual complementarity and is inherently robust to model heterogeneity, its high communication cost has limited its practical deployment, prompting most existing works to favor intermediate or late fusion. To address this, we propose a communication-efficient early collaborative perception framework that incorporates LiDAR completion to restore scene completeness under sparse transmission, dubbed as CoLC. Specifically, the CoLC integrates three complementary designs. First, each neighbor agent applies Foreground-Aware Point Sampling (FAPS) to selectively transmit informative points that retain essential structural and contextual cues under bandwidth constraints. The ego agent then employs Completion-Enhanced Early Fusion (CEEF) to reconstruct dense pillars from the received sparse inputs and adaptively fuse them with its own observations, thereby restoring spatial completeness. Finally, the Dense-Guided Dual Alignment (DGDA) strategy enforces semantic and geometric consistency between the enhanced and dense pillars during training, ensuring consistent and robust feature learning. Experiments on both simulated and real-world datasets demonstrate that CoLC achieves superior perception-communication trade-offs and remains robust under heterogeneous model settings.