Collaborative perception empowers each agent to improve its perceptual
ability through the exchange of perceptual messages with other agents. It
inherently results in a fundamental trade-off between perception ability and
communication cost. To address this bottleneck issue, our core idea is to
optimize the collaborative messages from two key aspects: representation and
selection. The proposed codebook-based message representation enables the
transmission of integer codes, rather than high-dimensional feature maps. The
proposed information-filling-driven message selection optimizes local messages
to collectively fill each agent's information demand, preventing information
overflow among multiple agents. By integrating these two designs, we propose
CodeFilling, a novel communication-efficient collaborative perception system,
which significantly advances the perception-communication trade-off and is
inclusive to both homogeneous and heterogeneous collaboration settings. We
evaluate CodeFilling in both a real-world dataset, DAIR-V2X, and a new
simulation dataset, OPV2VH+. Results show that CodeFilling outperforms previous
SOTA Where2comm on DAIR-V2X/OPV2VH+ with 1,333/1,206 times lower communication
volume. Our code is available at this https URL

通过交换感知信息来提高个体感知能力的协同感知方法在感知能力和通信成本之间存在着基本的权衡关系。优化感知信息的表示和选择是解决这个瓶颈问题的核心思想。基于码本的信息表示方案使得整数码的传输成为可能，而非高维特征图。信息填充驱动的信息选择方案优化了本地信息以满足每个个体的信息需求，避免了多个个体之间的信息溢出。通过整合这两个设计，我们提出了 CodeFilling，这是一个新颖的通信高效的协同感知系统，极大地改进了感知和通信之间的权衡关系，同时适用于同质和异质协作环境。我们在真实数据集 DAIR-V2X 和模拟数据集 OPV2VH + 上评估了 CodeFilling 的性能。结果显示，CodeFilling 在 DAIR-V2X/OPV2VH + 上的通信量比前沿方法 Where2comm 减少了 1333/1206 倍。我们的代码可以在此网址找到：https URL。