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cs.CV2026
SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout
Brian Cheong, Letian Wang, Sandro Papais +1
LiDAR-based tracking-by-attention (TBA) frameworks inherently suffer from high false negative errors, leading to a significant performance gap compared to traditional LiDAR-based t…
cs.CV2025
ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting
Sandro Papais, Letian Wang, Brian Cheong +1
We introduce ForeSight, a novel joint detection and forecasting framework for vision-based 3D perception in autonomous vehicles. Traditional approaches treat detection and forecast…
cs.CV2024
JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention
Brian Cheong, Jiachen Zhou, Steven Waslander
Tracking-by-detection (TBD) methods achieve state-of-the-art performance on 3D tracking benchmarks for autonomous driving. On the other hand, tracking-by-attention (TBA) methods ha…