  3D Multi-Object Tracking (MOT) captures stable and comprehensive motion
states of surrounding obstacles, essential for robotic perception. However,
current 3D trackers face issues with accuracy and latency consistency. In this
paper, we propose Fast-Poly, a fast and effective filter-based method for 3D
MOT. Building upon our previous work Poly-MOT, Fast-Poly addresses object
rotational anisotropy in 3D space, enhances local computation densification,
and leverages parallelization technique, improving inference speed and
precision. Fast-Poly is extensively tested on two large-scale tracking
benchmarks with Python implementation. On the nuScenes dataset, Fast-Poly
achieves new state-of-the-art performance with 75.8% AMOTA among all methods
and can run at 34.2 FPS on a personal CPU. On the Waymo dataset, Fast-Poly
exhibits competitive accuracy with 63.6% MOTA and impressive inference speed
(35.5 FPS). The source code is publicly available at
https://github.com/lixiaoyu2000/FastPoly.
