  A reliable and accurate 3D tracking framework is essential for predicting
future locations of surrounding objects and planning the observer's actions in
numerous applications such as autonomous driving. We propose a framework that
can effectively associate moving objects over time and estimate their full 3D
bounding box information from a sequence of 2D images captured on a moving
platform. The object association leverages quasi-dense similarity learning to
identify objects in various poses and viewpoints with appearance cues only.
After initial 2D association, we further utilize 3D bounding boxes
depth-ordering heuristics for robust instance association and motion-based 3D
trajectory prediction for re-identification of occluded vehicles. In the end,
an LSTM-based object velocity learning module aggregates the long-term
trajectory information for more accurate motion extrapolation. Experiments on
our proposed simulation data and real-world benchmarks, including KITTI,
nuScenes, and Waymo datasets, show that our tracking framework offers robust
object association and tracking on urban-driving scenarios. On the Waymo Open
benchmark, we establish the first camera-only baseline in the 3D tracking and
3D detection challenges. Our quasi-dense 3D tracking pipeline achieves
impressive improvements on the nuScenes 3D tracking benchmark with near five
times tracking accuracy of the best vision-only submission among all published
methods. Our code, data and trained models are available at
https://github.com/SysCV/qd-3dt.
