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：现有多试剂认知系统假定每个代理商都使用同一模型,具有相同的参数和结构。 性能可能因信任分数的错配而因不同的认知模型而退化。 在这项工作中,我们提议一个模型不可知的多试剂认知框架,以减少模型差异造成的消极影响,而不分享模型信息。 具体地说,我们提议一个信任校准器,可以消除预测信心分数的偏差。 每个代理商独立地在一个标准的公共数据库上进行这种校准,以保护知识产权。 我们还提议一个相应的捆绑箱组合算法,其中考虑到相邻箱的可信度分数和空间协议。 我们的实验揭示了不同代理商进行模型校准的必要性,结果显示拟议框架改善了不同代理商的基线三维天体探测功能。</s>