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