Glean: Recovering Underused Signal via Targeted Augmentation in Classical 3D Multi-Object Tracking

Classical tracking-by-detection pipelines for 3D multi-object tracking (MOT) provide a modular ap- proach to trajectory estimation, yet potentially useful information available around individual track- ing stages is often reduced to coarse representations or left unused. We present Glean, which sys- tematically exploits such underused information at selected stages of an established LiDAR-camera tracking pipeline while preserving its core association mechanism. Built on Fusion-Poly, Glean enriches detection scoring with contextual quality cues, replaces rectangular geometric alignment targets with pixel-level instance masks, adapts Kalman uncertainty based on ego speed, and re- fines completed trajectories through Gaussian smoothing. These components target complementary sources of information while leaving the underlying FACM association cascade unchanged. Only the detection-quality component requires additional training. On the nuScenes validation set, Glean improves AMOTA from 77.294 to 77.775 over the reproduced Fusion-Poly baseline, corresponding to a gain of 0.481%p. Across five alternating timing runs, Fusion-Poly and the Glean tracking loop with cached SAM2 masks both average 409.8 ms per frame, with run-level standard deviations of 3.5 ms and 4.1 ms, respectively; SAM2 inference is excluded from this comparison. Experimen- tal results demonstrate the effectiveness of the proposed Glean and its individual components, as evidenced by improvements in AMOTA.