  Existing multi-agent perception algorithms usually select to share deep
neural features extracted from raw sensing data between agents, achieving a
trade-off between accuracy and communication bandwidth limit. However, these
methods assume all agents have identical neural networks, which might not be
practical in the real world. The transmitted features can have a large domain
gap when the models differ, leading to a dramatic performance drop in
multi-agent perception. In this paper, we propose the first lightweight
framework to bridge such domain gaps for multi-agent perception, which can be a
plug-in module for most existing systems while maintaining confidentiality. Our
framework consists of a learnable feature resizer to align features in multiple
dimensions and a sparse cross-domain transformer for domain adaption. Extensive
experiments on the public multi-agent perception dataset V2XSet have
demonstrated that our method can effectively bridge the gap for features from
different domains and outperform other baseline methods significantly by at
least 8% for point-cloud-based 3D object detection.
