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.

本文提出了一种基于学习的 3D 物体跟踪方法，通过从时间点云数据中学习物体对应关系和运动信息的模式，利用 lidar 数据和运动估计技术进行跟踪，以及采用简单有效的速度平滑模块对物体的运动进行估计，从而在 KITTI 和更大规模的 Nuscenes 数据集上优于现有的 3D 跟踪方法。