3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In this paper, we present SimTrack to simplify the hand-crafted tracking paradigm by proposing an end-to-end trainable model for joint detection and tracking from raw point clouds. Our key design is to predict the first-appear location of each object in a given snippet to get the tracking identity and then update the location based on motion estimation. In the inference, the heuristic matching step can be completely waived by a simple read-off operation. SimTrack integrates the tracked object association, newborn object detection, and dead track killing in a single unified model. We conduct extensive evaluations on two large-scale datasets: nuScenes and Waymo Open Dataset. Experimental results reveal that our simple approach compares favorably with the state-of-the-art methods while ruling out the heuristic matching rules.

本研究提出了一种基于SimTrack的3D多目标跟踪系统，旨在简化手工制作的跟踪范例，通过使用端到端可训练的模型从原始点云中进行联合检测和跟踪，无需启发式匹配步骤，将跟踪对象的关联，新生对象的检测和死亡轨迹的消除集成为一个统一的模型。

自主驾驶中简单的三维多物体追踪探索