  Direct methods have shown excellent performance in the applications of visual
odometry and SLAM. In this work we propose to leverage their effectiveness for
the task of 3D multi-object tracking. To this end, we propose DirectTracker, a
framework that effectively combines direct image alignment for the short-term
tracking and sliding-window photometric bundle adjustment for 3D object
detection. Object proposals are estimated based on the sparse sliding-window
pointcloud and further refined using an optimization-based cost function that
carefully combines 3D and 2D cues to ensure consistency in image and world
space. We propose to evaluate 3D tracking using the recently introduced
higher-order tracking accuracy (HOTA) metric and the generalized intersection
over union similarity measure to mitigate the limitations of the conventional
use of intersection over union for the evaluation of vision-based trackers. We
perform evaluation on the KITTI Tracking benchmark for the Car class and show
competitive performance in tracking objects both in 2D and 3D.
