The KITTI Vision Benchmark Suite

End-to-end 3D object detection and tracking based
on point clouds is receiving more and more
attention in many robotics applications, such as
autonomous driving. Compared with 2D images, 3D
point clouds do not have enough texture
information for data association. Thus, we
propose an end-to- end point cloud-based network,
DiTNet, to directly assign a track ID to each
object across the whole sequence, without the
data association step. DiTNet is made location-
invariant by using relative location and
embeddings to learn each object’s spatial and
temporal features in the Spatio-temporal world.
The features from the detection module helps to
improve the tracking performance, and the
tracking module with final trajectories also
helps to refine the detection results.

Detailed in the paper.

@article{wang2021ditnet,
title={DiTNet: End-to-End 3D Object Detection
and Track ID Assignment in Spatio-Temporal
World},
author={Wang, Sukai and Cai, Peide and Wang,
Lujia and Liu, Ming},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={2},
pages={3397--3404},
year={2021},
publisher={IEEE}
}