The KITTI Vision Benchmark Suite

Traditional 3D MOT approaches face critical
challenges: geometric similarity metrics (e.g., IoU-
based) degrade at long ranges with high
computational costs, while distance-based methods
fail to capture object orientation and shape; the
effects of occlusion and the intricate relative ego-
object motion degrade tracking performance in
dynamic scenes. To this end, we propose PB-MOT, an
online framework integrating two key innovations:
ego-motion-compensated state estimation that
decouples dynamic interactions; and a rotated
ellipse association algorithm unifying pose and
shape-aware matching with adaptive distance
constraints. Evaluations on the KITTI benchmark show
that our PB-MOT achieves state-of-the-art
performance with a HOTA score of 81.94%, while
running at an impressive 2,402.76 FPS on CPU. This
enables real-time, high-fidelity perception and
tracking for resource-constrained robotic systems.

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@INPROCEEDINGS{11247375,
author={Pang, Bo and Xu, Yang and Chen, Jiming and
Li, Liang},
booktitle={2025 IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS)},
title={PB-MOT: Pose-aware Association Boosted
Online 3D Multi-Object Tracking},
year={2025},
volume={},
number={},
pages={14170-14177},
keywords={Measurement;Technological
innovation;Three-dimensional
displays;Tracking;Heuristic
algorithms;Dynamics;Robustness;Real-time
systems;Computational efficiency;State estimation},
doi={10.1109/IROS60139.2025.11247375}}