  Existing multi-agent perception systems assume that every agent utilizes the
same model with identical parameters and architecture. The performance can be
degraded with different perception models due to the mismatch in their
confidence scores. In this work, we propose a model-agnostic multi-agent
perception framework to reduce the negative effect caused by the model
discrepancies without sharing the model information. Specifically, we propose a
confidence calibrator that can eliminate the prediction confidence score bias.
Each agent performs such calibration independently on a standard public
database to protect intellectual property. We also propose a corresponding
bounding box aggregation algorithm that considers the confidence scores and the
spatial agreement of neighboring boxes. Our experiments shed light on the
necessity of model calibration across different agents, and the results show
that the proposed framework improves the baseline 3D object detection
performance of heterogeneous agents.
