Lightweight 3D Object Detection via Mamba-Based Knowledge Distillation

2026-08-04

AI Summary

This work addresses the trade-off between accuracy and efficiency in LiDAR-based 3D object detection for autonomous driving and robotic navigation by proposing a knowledge distillation framework built upon the Mamba architecture. The approach introduces a multi-branch teacher backbone and a box-aware, voxel-level feature transfer mechanism that enables selective feature alignment in voxel space, effectively distilling rich semantic knowledge from the teacher model into a lightweight student network. By focusing feature imitation on task-relevant regions, the method substantially reduces computational overhead while achieving detection accuracy comparable to state-of-the-art approaches on both public benchmarks and real-world datasets, thereby offering a favorable balance between deployment efficiency and performance.

Abstract

3D object detection using light detection and ranging (LiDAR) sensors requires a balance between accuracy and computational efficiency for onboard perception in autonomous driving and robotic navigation. Many existing LiDAR-based detection methods employ complex architectures to extract features, integrating large amounts of contextual information to enhance accuracy. This often results in significant computational costs, leading to suboptimal performance on resource-constrained embedded devices. In this study, we propose a knowledge distillation framework that transfers object-level voxel representations from a strong teacher model to lightweight student models through selective voxel-space feature alignment. Taking advantage of the linear-time sequence model with selective state spaces (Mamba), we design a multi-branch Mamba teacher backbone and a box-aware feature transfer mechanism that aligns spatially corresponding voxel features between teacher and student networks through a Mamba-based projection module. Experimental results on both a public dataset and real-world data show that our approach significantly reduces computational load while maintaining competitive accuracy compared with state-of-the-art methods.

Problem

• 3D object detection
• LiDAR
• computational efficiency
• lightweight models
• embedded devices

Innovation

• Mamba
• knowledge distillation
• lightweight 3D object detection
• voxel-space feature alignment
• LiDAR perception