視覚基盤モデルによる堅牢なマルチモーダル3D物体検出
Towards robust multimodal 3D object detection via visual foundation models
自動運転向けにSAMを微調整したSAM-ADとLiDAR特徴を融合し、ノイズや悪天候下でも堅牢な3D物体検出を実現するRoboDistillを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ziying Song, Lin Liu, Hongyu Pan, Shaoqing Xu, Lei Yang, Mingzhe Guo, Caiyan Jia
分類: cs.CV, cs.RO
原文アブストラクト
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, existing methods often fail to maintain robustness under out-of-distribution (OOD) corruptions caused by sensor noise, adverse weather, and environmental changes. To address this problem, we propose RoboDistill, a robust and generalizable multimodal 3D object detection framework that leverages visual foundation models (VFMs), such as the Segment Anything Model (SAM). First, we introduce SAM-AD, a domain-specific pretraining strategy that fine-tunes SAM on autonomous-driving imagery to extract feature representations with rich semantic information. Second, we design the AD Feature Pyramid Network (AD-FPN) to refine and upsample SAM features at multiple scales for seamless fusion with LiDAR features. Third, we develop the Depth-Guided Wavelet Attention (DGWA) module, which suppresses high-frequency sensor noise while preserving critical contextual information. Finally, we introduce KD Fusion, in which the pretrained SAM-AD serves as a teacher that distills high-quality visual knowledge into a lightweight point-cloud network, thereby improving robustness under noisy conditions. Extensive experiments across 27 challenging OOD corruption settings show that RoboDistill generally delivers stronger or competitive detection performance and robustness relative to representative state-of-the-art methods. This work bridges the gap between VFMs and 3D object detection and advances robust multimodal perception for real-world autonomous-driving applications.
