Communication-Efficient Collaborative Perception via Information Filling with Codebook
Authors: Yue Hu, Juntong Peng, Sifei Liu, Junhao Ge, Si Liu, Siheng Chen

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 1333/1206x lower communication volume. Our code is available at https://github.com/PhyllisH/CodeFilling.