  The diverse agents in multi-agent perception systems may be from different
companies. Each company might use the identical classic neural network
architecture based encoder for feature extraction. However, the data source to
train the various agents is independent and private in each company, leading to
the Distribution Gap of different private data for training distinct agents in
multi-agent perception system. The data silos by the above Distribution Gap
could result in a significant performance decline in multi-agent perception. In
this paper, we thoroughly examine the impact of the distribution gap on
existing multi-agent perception systems. To break the data silos, we introduce
the Feature Distribution-aware Aggregation (FDA) framework for cross-domain
learning to mitigate the above Distribution Gap in multi-agent perception. FDA
comprises two key components: Learnable Feature Compensation Module and
Distribution-aware Statistical Consistency Module, both aimed at enhancing
intermediate features to minimize the distribution gap among multi-agent
features. Intensive experiments on the public OPV2V and V2XSet datasets
underscore FDA's effectiveness in point cloud-based 3D object detection,
presenting it as an invaluable augmentation to existing multi-agent perception
systems.
