The KITTI Vision Benchmark Suite

First we build tracklets by calculating a
segmentation mask for each detection and linking
these over time using optical flow. We then fuse
these tracklets into 3D object reconstuctions
using depth and ego motion estimates. These 3D
reconstructions are then used to estimate the 3D
motion of objects, which is used to merge
tracklets into long-term tracks, bridging
occlusion gaps of up to 20 frames. This also
allows us to fill in missing detections.

Detections = TrackRCNN
Segmentations = BB2SegNet

@article{luiten2019MOTSFusion,
title={Track to Reconstruct and Reconstruct to
Track},
author={Luiten, Jonathon and Fischer, Tobias and
Leibe, Bastian},
journal={IEEE Robotics and Automation Letters},
year={2020},
publisher={IEEE}
}