用视觉生成虚拟点云，提升稀疏激光雷达追踪效果
MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues
- 融合图像检测结果生成稠密3D虚拟点，弥补激光雷达数据稀疏
- 在NuScenes数据集上显著提升追踪精度，优于现有方法
- 适合自动驾驶中激光雷达弱信号场景的追踪应用
3D单目标跟踪在自动驾驶和机器人领域至关重要。现有方法在点云稀疏和不完整情况下表现不佳。为此，我们提出多模态引导虚拟提示投影（MVCP）方案，通过生成虚拟提示来丰富稀疏点云。该方案将RGB传感器无缝集成到基于激光雷达的系统中，利用一组2D检测结果生成稠密的3D虚拟提示，显著改善点云稀疏性。这些虚拟提示可自然融入现有基于激光雷达的3D追踪器，带来显著性能提升。大量实验表明，该方法在NuScenes数据集上达到具有竞争力的性能。
原文摘要 · Abstract (English)
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.
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