Mambaベースの知識蒸留による軽量3D物体検出
Lightweight 3D Object Detection via Mamba-Based Knowledge Distillation
教師モデルから軽量な生徒モデルへ、Mambaを用いたボクセル特徴の選択的アライメントで知識を蒸留し、計算負荷を抑えつつ精度を維持する3D物体検出手法を提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Quoc Cuong Ninh, Huy Xuan Pham, Anh Tung Nguyen, Dinh Hoan Trinh
分類: cs.RO, cs.CV
原文アブストラクト
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.
