SAVTrack: Selective Vote Aggregation for Reliability-Aware Point Cloud Tracking

2026-09-15

AI Summary

为解决激光雷达点云中3D单目标跟踪在稀疏和不完整观测下的挑战，SAVTrack通过选择性投票聚合方法提高跟踪可靠性。

Abstract

3D single object tracking (SOT) in LiDAR point clouds is essential for autonomous systems, but remains challenging under sparse and incomplete observations. In such cases, different target points provide highly uneven constraints on the object center, causing some point-to-center votes to be substantially less reliable than others. Existing point-based trackers typically aggregate these hypotheses without explicitly modeling their reliability, allowing inaccurate votes to contaminate proposal clustering and degrade localization accuracy. To address this issue, we propose \textbf{SAVTrack}, a motion-aware tracking framework with \textbf{Selective Vote Aggregation (SAV)}. SAVTrack estimates the reliability of each candidate vote from both local seed features and inter-frame motion context, and removes low-confidence hypotheses before proposal clustering. This pre-aggregation gating prevents unreliable hypotheses from affecting cluster formation while introducing only modest computational overhead. SAVTrack achieves competitive performance on KITTI and nuScenes, reaching 68.4/87.4 and 58.44/69.82 Success/Precision, respectively, while running at 82 FPS. It retains fewer than one-sixth of the candidate votes used by dense aggregation and remains particularly effective under sparse target observations.

Problem

• 3D single object tracking
• LiDAR point clouds
• sparse observations
• reliability
• vote aggregation

Innovation

• Selective Vote Aggregation
• Reliability-Aware
• Point Cloud Tracking
• Sparse Observations