Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation

ArXi:2409.11018v2 Announce Type: replace 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 detection models depend on complex feature transformations, leading to poor real-time performance and high resource consumption, which limits their practical effectiveness. In this work, we propose a faster LiDAR 3D object detector, a framework that adaptively aligns sparse voxels to enable efficient heterogeneous knowledge distillation, called