Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To alleviate adverse impacts of pose errors, we propose CoAlign, a novel hybrid collaboration framework that is robust to unknown pose errors. The proposed solution relies on a novel agent-object pose graph modeling to enhance pose consistency among collaborating agents. Furthermore, we adopt a multi-scale data fusion strategy to aggregate intermediate features at multiple spatial resolutions. Comparing with previous works, which require ground-truth pose for training supervision, our proposed CoAlign is more practical since it doesn't require any ground-truth pose supervision in the training and makes no specific assumptions on pose errors. Extensive evaluation of the proposed method is carried out on multiple datasets, certifying that CoAlign significantly reduce relative localization error and achieving the state of art detection performance when pose errors exist. Code are made available for the use of the research community at https://github.com/yifanlu0227/CoAlign.

翻译：协同3D目标检测通过多智能体间的信息交换，在存在遮挡等传感器缺陷的情况下提升目标检测精度。然而实际应用中，由于定位不完美导致的位姿估计误差会引起空间消息错位，显著降低协同性能。为减轻位姿误差的不利影响，我们提出CoAlign——一种新型混合协同框架，对未知位姿误差具有鲁棒性。该解决方案基于创新的智能体-目标位姿图建模，可增强协同智能体间的位姿一致性。此外，我们采用多尺度数据融合策略，在多个空间分辨率下聚合中间特征。与需要真实位姿作为训练监督的先前工作相比，所提出的CoAlign方法更为实用：其训练过程无需任何真实位姿监督，且不对位姿误差做特定假设。我们在多个数据集上对该方法进行了广泛评估，验证了CoAlign能显著降低相对定位误差，并在存在位姿误差时取得最优检测性能。研究社区可使用我们在https://github.com/yifanlu0227/CoAlign 上公开的代码。