CEST: Enhancing Multi-Agent Perception via Communication-Efficient Spatial–Temporal Fusion

Collaborative perception enables multiple agents to exchange and integrate information, achieving a more comprehensive understanding of the surrounding environment. While it holds great potential to mitigate object occlusion and compensate for individual agents’ limited field of view, several challenges remain. Among them are localization noise and transmission delays in complex environments, which may cause spatial and temporal misalignment of shared features. Additionally, limited communication bandwidth hinders the effective utilization of collaborative information, resulting in suboptimal feature aggregation. To tackle these challenges, we present CEST, a Communication-Efficient Spatial-Temporal fusion framework that jointly considers historical context and spatial misalignment while reducing communication overhead. First, we present a temporal context aggregation module that encodes temporal information and captures collaborators’ temporal context to enhance current feature estimation. Then, we adopt a query-aware communication mechanism that leverages both channel and spatial queries to identify visually essential and complementary information, while incorporating a mutual information loss to preserve discriminative features. Finally, we design a spatial-temporal aggregation module that enables efficient fusion by integrating multi-scale spatial representations and extracting contextual cues across agents. To thoroughly assess our framework, we conduct experiments on both simulated and real-world collaborative 3D object detection datasets. The results confirm that our approach maintains robust performance across varying conditions, including pose errors, transmission delays, and bandwidth limitations.