The KITTI Vision Benchmark Suite

Graph-enhanced PMB is a LiDAR-based online 3D
multi-object tracking method built upon the
Poisson Multi-Bernoulli filtering framework. A
graph neural network is introduced to learn
discriminative affinities between predicted tracks
and current detections, improving data association
in crowded and ambiguous scenes. In addition, a
neural-enhanced motion model combines a CTRA
physical prior with recurrent residual prediction
to better handle nonlinear target motion. A
lifecycle-aware management strategy is further
employed to improve track initialization, missed-
detection handling, and termination. The method
preserves probabilistic PMB inference while
enhancing association, motion prediction, and
track management with learned components.

Detector: PointRCNN
Object class: Car
Frame interval: 0.1 s
Tracking model: CTRA

Detection threshold: 0.8
NMS IoU threshold: 0.1
NMS maximum distance: 10.0 m

Survival probability: 1.0
Detection probability: 0.9
Minimum detection-probability scaling: 0.7
Detection-probability scaling point: 10

Acceleration deviation: 1.0
Turn-rate noise deviation: 0.15
Jerk deviation: 0.5
Measurement deviation: [0.5, 0.5, 1.0, 1.0, 0.5]

MBM gating distance: 4.0 m
PPP gating distance: 4.0 m
Used-measurement distance threshold: 1.0 m
Used-measurement likelihood threshold: 0.5
PPP maximum survival step: 1

Clutter rate: 10
Detected birth intensity: 0.2
Undetected birth intensity: 0.5
Maximum adaptive birth intensity: 0.2

Confidence threshold: 1.2
Track extraction probability threshold: 0.85
Track termination probability threshold: 0.9
Maximum unmatched extraction frames: 5

ID refinement maximum age: 2 frames
ID refinement base distance: 2.5 m
ID refinement distance increment: 1.5 m

@unpublished{xu2026graphpmb,
title = {Graph-enhanced PMB for LiDAR-based 3D
Multi-Object Tracking},
author = {JingLiang,Xu},
year = {2026},
note = {Manuscript under review}
}