MVCTrack：通过多模态引导的虚拟线索提升3D点云跟踪
MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues
AI总结：
针对3D点云跟踪在稀疏场景下的局限，提出多模态引导虚拟线索投影（MVCP）方案，利用2D检测生成密集3D虚拟线索以丰富点云，并构建增强型跟踪器MVCTrack，在NuScenes数据集上取得竞争性性能。
摘要：
3D单目标跟踪在自动驾驶和机器人技术中至关重要。现有方法在稀疏且不完整的点云场景中常面临困难。为解决这些局限，我们提出一种多模态引导虚拟线索投影（MVCP）方案，通过生成虚拟线索来丰富稀疏点云。此外，我们基于生成的虚拟线索引入了增强型跟踪器MVCTrack。具体而言，MVCP方案将RGB传感器无缝集成到基于LiDAR的系统中，利用一组2D检测结果创建密集的3D虚拟线索，显著改善了点云的稀疏性。这些虚拟线索可与现有基于LiDAR的3D跟踪器自然结合，带来显著的性能提升。大量实验表明，我们的方法在NuScenes数据集上取得了具有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
