GM3DMOT: Online 3D Graph-Mamba Multi-Object Tracking

Online LiDAR 3D multi-object tracking requires reliable association over sparse, unordered, and dynamically changing detections. Existing learning-based geometry-aware methods enrich association representations through sparse graph propagation or dense inter-object interactions. However, sparse graphs capture limited context, while dense interactions may introduce interference from weakly related objects. We propose GM3DMOT, an online LiDAR 3D MOT framework for identity-aware local and frame-level context modeling. Its Geometry-Conditioned Object Graph-Mamba Encoder comprises two complementary branches. The Same-Class Local Interaction Branch uses a same-class BEV graph with edge-gated message passing to model local ambiguity among nearby identity competitors. The Score-Degree Mamba Scan Branch combines detection confidence with same-class graph in-degree to serialize unordered detections for broader frame-level propagation. Scan-path geometric cues encode BEV distance and class consistency between consecutive detections in the serialized sequence. Before association decoding, a lightweight motion-conditioned relation refinement introduces short-term detection–track consistency. On nuScenes, GM3DMOT achieves 73.9% AMOTA with 138 identity switches on validation and 70.3% AMOTA with 198 identity switches on test.