Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage in leveraging the long-term temporal context representation. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero. Concretely, an offline tracker coupled with a multi-frame detector is proposed to focus on the completeness of generated object tracks. An attention-mechanism refining module is proposed to strengthen contextual information interaction across long-term sequential point clouds for object refining with decomposed regression methods. Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods. Notably, DetZero ranks 1st place on Waymo 3D object detection leaderboard with 85.15 mAPH (L2) detection performance. Further experiments validate the application of taking the place of human labels with such high-quality results. Our empirical study leads to rethinking conventions and interesting findings that can guide future research on offboard 3D object detection.

翻译：现有离线3D检测器通常遵循模块化流水线设计，以充分利用无限时序点云。我们发现，离线3D检测器的全部潜力尚未被充分挖掘，主要原因有二：（1）车载多目标跟踪器无法生成足够完整的物体轨迹；（2）物体的运动状态为基于物体中心的精炼阶段利用长时域上下文表征带来了不可避免的挑战。为解决这些问题，我们提出一种全新的离线3D目标检测范式——DetZero。具体而言，我们提出将离线跟踪器与多帧检测器相结合，以提升生成物体轨迹的完整性；同时提出基于注意力机制的精炼模块，通过解耦回归方法强化长时序点云间的上下文信息交互以精炼物体检测结果。在Waymo开放数据集上的大量实验表明，我们的DetZero优于所有最先进的车载与离线3D检测方法。值得注意的是，DetZero以85.15 mAPH（L2）的检测性能在Waymo 3D目标检测排行榜上位列第一。进一步实验验证了用此类高质量结果替代人工标注的可行性。我们的实证研究引发了对相关惯例的重新思考，并得出若干有趣发现，可为未来离线3D目标检测研究提供指导。