  3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle
perception module. Most methods optimize object detection and data association
independently. These methods make the network structure complicated and limit
the improvement of MOT accuracy. we proposed a 3D MOT framework based on
simultaneous optimization of object detection and scene flow estimation. In the
framework, a detection-guidance scene flow module is proposed to relieve the
problem of incorrect inter-frame assocation. For more accurate scene flow label
especially in the case of motion with rotation, a box-transformation-based
scene flow ground truth calculation method is proposed. Experimental results on
the KITTI MOT dataset show competitive results over the state-of-the-arts and
the robustness under extreme motion with rotation.
