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 https://github.com/YuanYunshuang/CoSense3D

由于有效减少遮挡并扩展视野，群体感知近年来引起了广泛关注，从而提高可靠性、效率和决策安全性。本文提出了一种基于代理的训练框架，通过将深度学习模块和代理数据分开处理，实现了更清晰的数据流结构，既提供了用于灵活原型化数据处理流程和定义每个代理的梯度计算的 API，又提供了与用户交互式训练、测试和数据可视化的界面。在突出的群体感知基准 OPV2V 上，通过四个群体目标检测模型的训练实验结果表明，基于代理的训练可以显著减少 GPU 内存消耗和训练时间，并保持推理性能。