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 this https URL

提出了空间置信图概念，以实现基于多智能体协同感知的通信高效性，使智能体在信息共享时只传递关键的感知信息，优化感知性能，并可自适应调整传输空间区域以应对不同的通信带宽。在多个数据集上的 3D 物体检测任务中，Where2comm 算法分别以不同的传感器类型和车辆类型获得了优异的性能，并在通信体积方面实现了大幅度降低。