Data association is a knotty problem for 2D Multiple Object Tracking due to
the object occlusion. However, in 3D space, data association is not so hard.
Only with a 3D Kalman Filter, the online object tracker can associate the
detections from LiDAR. In this paper, we rethink the data association in 2D MOT
and utilize the 3D object representation to separate each object in the feature
space. Unlike the existing depth-based MOT methods, the 3D object
representation can be jointly learned with the object association module.
Besides, the object's 3D representation is learned from the video and
supervised by the 2D tracking labels without additional manual annotations from
LiDAR or pretrained depth estimator. With 3D object representation learning
from Pseudo 3D object labels in monocular videos, we propose a new 2D MOT
paradigm, called P3DTrack. Extensive experiments show the effectiveness of our
method. We achieve new state-of-the-art performance on the large-scale Waymo
Open Dataset.

该论文探讨了在 2D 多目标跟踪中的数据关联问题，并借鉴 3D 目标表示来解决这一问题，提出了一种新的基于 Pseudo 3D 标签的 2D MOT 方法 P3DTrack，通过视频数据自学习 3D 目标表示，并在 Waymo 开放数据集上达到了最新的最佳性能。