This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label the objects with clear shapes in a track, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such a design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer to this characteristic as "onCe detecTed, neveR Lost" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and the previous state-of-the-art methods in the highly competitive Waymo Open Dataset without model ensemble. The code will be made publicly available at https://github.com/tusen-ai/SST.

翻译：本文旨在实现高性能的离线激光雷达三维目标检测。我们首先观察到，经验丰富的人类标注者从轨迹中心视角进行标注：他们先标注轨迹中形状清晰的目标，再利用时间连续性推断模糊目标的标注。受此启发，我们提出了一种基于轨迹中心视角的高性能离线检测器，而非传统的目标中心视角。该方法包含双向跟踪模块和轨迹中心学习模块。这种设计使得当目标在某一时刻被检测到时，检测器能够推断并优化完整的轨迹。我们将这一特性称为“一旦检测，永不丢失”，并将所提系统命名为CTRL。大量实验证明了我们方法的卓越性能——在竞争激烈的Waymo开放数据集上，无需模型集成即可超越人类级别的标注精度以及先前最先进的方法。代码将在https://github.com/tusen-ai/SST公开。