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.

通过引入 Feature Distribution-aware Aggregation (FDA) 框架以缓解多智能体感知中的分布差异，本论文全面探讨了分布差异对现有多智能体感知系统的影响，并通过在公共 OPV2V 和 V2XSet 数据集上进行的大量实验验证了 FDA 在基于点云的 3D 目标检测中的有效性，将其视为现有多智能体感知系统中无价的增强手段。