MVCTrack: マルチモーダル誘導仮想キューによる3D点群追跡の強化
MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues
LiDAR点群の疎性を補うため、RGB画像から得た2D検出を仮想3Dキューに変換して点群を密にし、既存の3D追跡器の性能を向上させる手法を提案。
著者: Zhaofeng Hu, Sifan Zhou, Zhihang Yuan, Dawei Yang, Shibo Zhao, Ci-Jyun Liang
分類: cs.CV, cs.RO
原文アブストラクト
3D single object tracking is essential in autonomous driving and robotics. Existing methods often struggle with sparse and incomplete point cloud scenarios. To address these limitations, we propose a Multimodal-guided Virtual Cues Projection (MVCP) scheme that generates virtual cues to enrich sparse point clouds. Additionally, we introduce an enhanced tracker MVCTrack based on the generated virtual cues. Specifically, the MVCP scheme seamlessly integrates RGB sensors into LiDAR-based systems, leveraging a set of 2D detections to create dense 3D virtual cues that significantly improve the sparsity of point clouds. These virtual cues can naturally integrate with existing LiDAR-based 3D trackers, yielding substantial performance gains. Extensive experiments demonstrate that our method achieves competitive performance on the NuScenes dataset.
