Adaptation-Free Heterogeneous Collaborative Perception with Unseen Agent Configurations
For collaborative perception systems, this work addresses the practical problem of adapting to unseen agent configurations without retraining, achieving strong performance gains.
ALF enables zero-adaptation collaborative perception with unseen agent configurations, outperforming the strongest prior baseline by 35.91% in relative mAP@0.7 on V2X-Real while requiring only 120 bytes per agent per frame.
Collaborative perception improves 3D object detection by enabling agents to share complementary observations, but most existing methods assume fixed or known collaborator encoder configurations, limiting deployment in practice. In this work, we consider an open-world setting in which auxiliary agents with unseen configurations may appear after deployment, such as different LiDAR beam counts or encoder architectures. To address this challenge, we propose ALF, a collaborative perception framework that enables zero-adaptation collaboration with unseen agent configurations by lifting lightweight box-level messages into ego-compatible auxiliary features. ALF converts auxiliary box-level messages into pseudo-BEV maps and synthesizes ego-compatible latent features by combining object-centric cues with scene context from the ego feature. On V2X-Real, under a zero-shot evaluation across 64 case studies, ALF outperforms the strongest prior baseline by 35.91% in relative mAP@0.7 while requiring only 120 bytes per agent per frame (approximately 9.6 Kbps bandwidth at 10 Hz).