HAFNet: Hybrid-Stage Collaborative Perception via Agent-Foreground List

The inevitable trade-off between perceptual performance and communication bandwidth in collaborative perception poses a significant challenge. To mitigate this constraint, we introduceHAFNet, a hybrid-stage collaborative perception method designed for multi-agent collaborative 3D object detection. This method initially generates dense proposal, serving as the Region of Interest (RoI). Next, all proposals are aggregated through the proposed Agent-Foreground List via RoI association. Moreover, secondary sampling is performed according to those foreground regions. Furthermore, we achieve superior feature extraction through geometric and offset encoding. Concurrently, the setting of proxy points effectively reduces the size of the collective perception messages. In the end, those features are fused and interacted to get the detection. Extensive experiments on existing DAIR-V2X and V2V4Real datasets illustrate thatHAFNetalmost keeps a minimum communication bandwidth, while it surpasses existing state-of-the-art methods in 3D object detection tasks.