IsoComm: Heterogeneous Cooperative Perception Across Training-Isolated Systems

Heterogeneous cooperative perception enables agents with different sensing modalities and perception models to collaborate by sharing complementary information. Recent methods have moved heterogeneous cooperation closer to real-world deployment by relaxing assumptions on modalities, architectures, and system extensibility. However, cross-agent compatibility is still commonly established before deployment through shared source training domains, coupled model components, or pre-established representations and communication protocols. We study a more open setting where complete perception systems are developed independently on private domains and future collaborators are unknown. We formulate this setting as $\text{Training-Isolated Heterogeneous Cooperative Perception}$. Under this setting, conventional direct fusion cannot reliably turn an unseen sender into a cooperative gain, because the sender representation and receiver fusion interface have been learned through separate development processes. To address this challenge, we propose $\text{IsoComm}$ with two mechanisms: Protocol-Basis Diversification (PBD) and Ownership-Preserving Attention (OPA). PBD diversifies the communication bases encountered during local training and reduces dependence on a specific local basis. OPA preserves sender-native value encoding while keeping relevance estimation and aggregation under receiver control. This design enables zero-shot cooperation between independently trained systems without pair-specific optimization. For persistent partnerships, we further introduce Communication-Aware Commissioning to calibrate lightweight communication interfaces while keeping both perception systems frozen. Across three unseen senders, IsoComm consistently improves over the ego-only receiver, achieving 88.32–88.54 $A{P}_{50}$ versus 82.49 for ego-only, while reducing the cross-sender $A{P}_{50}$ range to 0.22 points and supporting annotation-efficient commissioning.