MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues
This addresses tracking challenges in autonomous driving and robotics, but it is incremental as it builds on existing LiDAR-based trackers.
The paper tackles the problem of 3D single object tracking in sparse and incomplete point cloud scenarios by proposing a multimodal-guided virtual cues projection scheme to enrich point clouds, resulting in competitive performance on the NuScenes dataset.
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.