  Recent machine learning-based multi-object tracking (MOT) frameworks are
becoming popular for 3-D point clouds. Most traditional tracking approaches use
filters (e.g., Kalman filter or particle filter) to predict object locations in
a time sequence, however, they are vulnerable to extreme motion conditions,
such as sudden braking and turning. In this letter, we propose PointTrackNet,
an end-to-end 3-D object detection and tracking network, to generate foreground
masks, 3-D bounding boxes, and point-wise tracking association displacements
for each detected object. The network merely takes as input two adjacent
point-cloud frames. Experimental results on the KITTI tracking dataset show
competitive results over the state-of-the-arts, especially in the irregularly
and rapidly changing scenarios.
