用Transformer实现激光雷达3D多目标跟踪，提升密集场景下追踪精度。
LiDAR MOT-DETR: A LiDAR-based Two-Stage Transformer for 3D Multiple Object Tracking
- 两阶段架构：先优化检测，再通过注意力关联跨帧目标
- 在线模式下nuScenes上aMOTA达0.724，比基线高得多
- 适合自动驾驶中复杂动态环境的实时目标追踪
基于激光雷达点云的多目标跟踪面临数据稀疏不规则、需保持时间连贯性的挑战。传统系统依赖手工特征和运动模型，在密集或高速场景中易丢失目标身份。本文提出一种受DETR启发的两阶段激光雷达跟踪框架：第一阶段在移动时间窗口内对任意现成检测器输出进行检测优化；第二阶段利用DETR式注意力块，以点云为上下文，将已跟踪目标与优化后的检测结果关联，实现跨帧跟踪。模型在nuScenes和KITTI数据集上以在线与离线（前向窥探）模式训练，性能优异。数值结果显示，在nuScenes数据集上，在线模式的aMOTA达到0.724，aMOTP为0.475，优于纯激光雷达基线及当前最先进模型；离线模式额外提升3个百分点的aMOTP。
原文摘要 · Abstract (English)
Multi-object tracking from LiDAR point clouds presents unique challenges due to the sparse and irregular nature of the data, compounded by the need for temporal coherence across frames. Traditional tracking systems often rely on hand-crafted features and motion models, which can struggle to maintain consistent object identities in crowded or fast-moving scenes. We present a lidar-based two-staged DETR inspired transformer; a smoother and tracker. The smoother stage refines lidar object detections, from any off-the-shelf detector, across a moving temporal window. The tracker stage uses a DETR-based attention block to maintain tracks across time by associating tracked objects with the refined detections using the point cloud as context. The model is trained on the datasets nuScenes and KITTI in both online and offline (forward peeking) modes demonstrating strong performance across metrics such as ID-switch and multiple object tracking accuracy (MOTA). The numerical results indicate that the online mode outperforms the lidar-only baseline and SOTA models on the nuScenes dataset, with an aMOTA of 0.724 and an aMOTP of 0.475, while the offline mode provides an additional 3 pp aMOTP.
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