Repository files navigation

Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D using quasi-dense object proposals from 2D images.

Monocular Quasi-Dense 3D Object Tracking,
Hou-Ning Hu, Yung-Hsu Yang, Tobias Fischer, Trevor Darrell, Fisher Yu, Min Sun,
arXiv technical report (arXiv 2103.07351)Project Website (QD-3DT)

@article{Hu2021QD3DT,    author = {Hu, Hou-Ning and Yang, Yung-Hsu and Fischer, Tobias and Yu, Fisher and Darrell, Trevor and Sun, Min},    title = {Monocular Quasi-Dense 3D Object Tracking},    journal = {ArXiv:2103.07351},    year = {2021}}

A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer’s actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform. The object association leverages quasi-dense similarity learning to identify objects in various poses and viewpoints with appearance cues only. After initial 2D association, we further utilize 3D bounding boxes depth-ordering heuristics for robust instance association and motion-based 3D trajectory prediction for re-identification of occluded vehicles. In the end, an LSTM-based object velocity learning module aggregates the long-term trajectory information for more accurate motion extrapolation. Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios. On the Waymo Open benchmark, we establish the first camera-only baseline in the 3D tracking and 3D detection challenges. Our quasi-dense 3D tracking pipeline achieves impressive improvements on the nuScenes 3D tracking benchmark with near five times tracking accuracy of the best vision-only submission among all published methods.

We achieved the best vision-only submission

We established the first camera-only baseline on Waymo Open

Please refer toINSTALL.md for installation and toDATA.md dataset preparation.

Please seeGETTING_STARTED.md for the basic usage of QD-3DT.

Please refer toMODEL_ZOO.md for reproducing the results on varients of benchmarks

This repo is currently maintained by Hou-Ning Hu (@eborboihuc), Yung-Hsu Yang (@RoyYang0714), and Tobias Fischer (@tobiasfshr).

This work is licensed under BSD 3-Clause License. SeeLICENSE for details.Third-party datasets and tools are subject to their respective licenses.

We thankJiangmiao Pang for his help in providing theqdtrack codebase inmmdetection. This repo usespy-motmetrics for MOT evaluation,waymo-open-dataset for Waymo Open 3D detection and 3D tracking task, andnuscenes-devkit for nuScenes evaluation and preprocessing.