Collective Perception has attracted significant attention in recent years due to its advantage for mitigating occlusion and expanding the field-of-view, thereby enhancing reliability, efficiency, and, most crucially, decision-making safety. However, developing collective perception models is highly resource demanding due to extensive requirements of processing input data for many agents, usually dozens of images and point clouds for a single frame. This not only slows down the model development process for collective perception but also impedes the utilization of larger models. In this paper, we propose an agent-based training framework that handles the deep learning modules and agent data separately to have a cleaner data flow structure. This framework not only provides an API for flexibly prototyping the data processing pipeline and defining the gradient calculation for each agent, but also provides the user interface for interactive training, testing and data visualization. Training experiment results of four collective object detection models on the prominent collective perception benchmark OPV2V show that the agent-based training can significantly reduce the GPU memory consumption and training time while retaining inference performance. The framework and model implementations are available at \url{https://github.com/YuanYunshuang/CoSense3D}

翻译：集体感知因其在缓解遮挡、扩大视野范围方面的优势，近年来受到广泛关注，从而提升了可靠性、效率，以及最关键的决策安全性。然而，开发集体感知模型对资源需求极高，因为需要处理来自多个智能体的输入数据，通常单帧就涉及数十张图像和点云。这不仅减缓了集体感知模型的开发进程，也阻碍了更大模型的利用。在本文中，我们提出一种基于智能体的训练框架，该框架分别处理深度学习模块和智能体数据，以形成更清晰的数据流结构。该框架不仅提供了用于灵活构建数据处理流水线和定义各智能体梯度计算的API，还提供了用于交互式训练、测试和数据可视化的用户界面。在著名集体感知基准OPV2V上对四种集体目标检测模型的训练实验结果表明，基于智能体的训练能在保持推理性能的同时显著降低GPU内存消耗和训练时间。该框架及模型实现已发布于\url{https://github.com/YuanYunshuang/CoSense3D}。