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.

翻译：现有的多智能体感知系统假设每个智能体使用具有相同参数和架构的模型。当采用不同感知模型时，由于置信度分数不匹配，系统性能可能下降。本文提出一种模型无关的多智能体感知框架，在不共享模型信息的情况下减少模型差异带来的负面影响。具体而言，我们提出一种置信度校准器，能够消除预测置信度分数偏差。每个智能体在标准公共数据库上独立执行此校准，以保护知识产权。同时，我们提出一种相应的边界框聚合算法，该算法综合考虑相邻边界框的置信度分数与空间一致性。实验揭示了不同智能体间进行模型校准的必要性，结果表明所提框架能够提升异构智能体的基线3D目标检测性能。