Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest thanks to the ability to enhance the perception of autonomous vehicles via deep information fusion with intelligent roadside units (RSU), thus minimizing the impact of occlusion. While significant advancement has been made, the data-hungry nature of these methods creates a major hurdle for their real-world deployment, particularly due to the need for annotated RSU data. Manually annotating the vast amount of RSU data required for training is prohibitively expensive, given the sheer number of intersections and the effort involved in annotating point clouds. We address this challenge by devising a label-efficient object detection method for RSU based on unsupervised object discovery. Our paper introduces two new modules: one for object discovery based on a spatial-temporal aggregation of point clouds, and another for refinement. Furthermore, we demonstrate that fine-tuning on a small portion of annotated data allows our object discovery models to narrow the performance gap with, or even surpass, fully supervised models. Extensive experiments are carried out in simulated and real-world datasets to evaluate our method.

翻译：遮挡给自动驾驶等安全关键应用带来了重大挑战。协作感知凭借与智能路侧单元（RSU）进行深度信息融合来增强自动驾驶车辆感知能力、从而最小化遮挡影响，近年来吸引了广泛研究兴趣。尽管已取得显著进展，但这些方法对数据的高度依赖成为其实际部署的主要障碍，尤其是需要对路侧单元数据进行标注。考虑到路口数量庞大以及点云标注所需的人工投入，为训练而手动标注海量路侧单元数据代价高昂。我们通过设计一种基于无监督目标发现的标签高效路侧单元目标检测方法来解决这一挑战。本文引入两个新模块：一个基于点云时空聚合的目标发现模块，另一个用于精细化处理。此外，我们证明，在少量标注数据上进行微调可使我们的目标发现模型缩小甚至超越全监督模型的性能差距。我们在仿真和真实数据集上进行了广泛实验以评估所提方法。