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cs.CV2026
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation
Rui Yu, Runkai Zhao, Jiagen Li +3
The LiDAR 3D object detector that strikes a balance between accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many existing LiDAR det…
cs.CV2025
From Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Tracking
Yuqing Shao, Yuchen Yang, Rui Yu +5
End-to-end multi-object tracking (MOT) methods have recently achieved remarkable progress by unifying detection and association within a single framework. Despite their strong dete…
cs.CV2024
Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences
Rui Yu, Runkai Zhao, Cong Nie +3
Accurate and robust LiDAR 3D object detection is essential for comprehensive scene understanding in autonomous driving. Despite its importance, LiDAR detection performance is limit…