论文标题
从模式进行跟踪：3D对象跟踪的点云中学习相应的模式
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking
论文作者
论文摘要
坚固的3D对象跟踪器，它不断跟踪周围的物体并估算其轨迹是自动驾驶车辆的关键。大多数现有的跟踪方法采用逐个检测策略，通常需要复杂的成对相似性计算，而忽略了连续对象运动的性质。在本文中，我们建议直接从时间点云数据中学习3D对象对应，并从对应模式中推断运动信息。我们修改标准3D对象检测器同时处理两个激光镜帧，并预测关联和运动估计任务的边界框对。我们还为管道配备了一个简单而有效的速度平滑模块，以估算一致的物体运动。从学到的对应关系和运动的完善中，我们的方法超过了Kitti和大型Nuscenes数据集的现有3D跟踪方法。
A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles. Most existing tracking methods employ a tracking-by-detection strategy, which usually requires complex pair-wise similarity computation and neglects the nature of continuous object motion. In this paper, we propose to directly learn 3D object correspondences from temporal point cloud data and infer the motion information from correspondence patterns. We modify the standard 3D object detector to process two lidar frames at the same time and predict bounding box pairs for the association and motion estimation tasks. We also equip our pipeline with a simple yet effective velocity smoothing module to estimate consistent object motion. Benifiting from the learned correspondences and motion refinement, our method exceeds the existing 3D tracking methods on both the KITTI and larger scale Nuscenes dataset.
