论文标题
其中2Comm：通过空间信心图的沟通效率协作感知
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
论文作者
论文摘要
多机构协作感知可以通过使代理商能够通过交流相互共享互补信息来显着升级感知表现。它不可避免地会导致感知表现与沟通带宽之间的基本权衡。为了解决这个瓶颈问题，我们提出了一个空间置信图，该图反映了感知信息的空间异质性。它使代理只能在空间上共享稀疏但感知重要的信息，从而有助于沟通。基于这张新型的空间置信图，我们提出了一个沟通效率高效的协作感知框架2Comm。其中2Comm具有两个不同的优势：i）它考虑了实用的压缩，并使用较少的沟通来通过专注于感知至关重要的领域来实现更高的感知表现； ii）它可以通过动态调整涉及通信的空间区域来处理不同的通信带宽。要评估2comm，我们在四个数据集上使用两种模式（相机/激光镜）和两种模态（相机/激光镜）和两种代理类型（CAR/无人机）中考虑3D对象检测：OPV2V，V2X-SIM，DAIR-V2X和我们的原始coperception-uavs。其中2comm始终优于先前的方法；例如，它实现了超过$ 100,000 \ times $较低的通信量，并且在OPV2V上仍然优于脱颖而出和V2X-VIT。我们的代码可在https://github.com/mediabrain-sjtu/where2comm上找到。
Multi-agent collaborative perception could significantly upgrade the perception performance by enabling agents to share complementary information with each other through communication. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. To tackle this bottleneck issue, we propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. It empowers agents to only share spatially sparse, yet perceptually critical information, contributing to where to communicate. Based on this novel spatial confidence map, we propose Where2comm, a communication-efficient collaborative perception framework. Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication. To evaluate Where2comm, we consider 3D object detection in both real-world and simulation scenarios with two modalities (camera/LiDAR) and two agent types (cars/drones) on four datasets: OPV2V, V2X-Sim, DAIR-V2X, and our original CoPerception-UAVs. Where2comm consistently outperforms previous methods; for example, it achieves more than $100,000 \times$ lower communication volume and still outperforms DiscoNet and V2X-ViT on OPV2V. Our code is available at https://github.com/MediaBrain-SJTU/where2comm.
