通过动态压缩点云冗余信息，实现高效高精度3D目标跟踪。
CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking
- 基于信息瓶颈原理，动态压缩前景点云冗余，保留关键特征。
- 在KITTI等数据集上达到90帧/秒实时速度，精度领先。
- 适合需要低延迟、高精度的自动驾驶点云跟踪场景。
激光雷达点云中的3D单目标跟踪是计算机视觉与自动驾驶的关键任务。尽管已有显著进展，点云固有的稀疏性带来双重冗余问题：(1) 背景噪声导致的空间冗余影响精度；(2) 前景内部的信息冗余制约效率。为此，我们提出CompTrack，一个端到端框架，系统性消除两类冗余。首先，引入空间前景预测模块（SFP）基于信息熵过滤无关背景噪声，解决空间冗余；其次，核心为信息瓶颈引导的动态令牌压缩（IB-DTC）模块，通过在线SVD分析，基于低秩近似自适应将前景冗余压缩为紧凑且高信息量的代理令牌。在KITTI、nuScenes和Waymo数据集上的大量实验表明，CompTrack在保持顶级追踪性能的同时，实现在单张RTX 3090 GPU上以90帧/秒的实时速度运行。
原文摘要 · Abstract (English)
3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsity of point clouds introduces a dual-redundancy challenge that limits existing trackers: (1) vast spatial redundancy from background noise impairs accuracy, and (2) informational redundancy within the foreground hinders efficiency. To tackle these issues, we propose CompTrack, a novel end-to-end framework that systematically eliminates both forms of redundancy in point clouds. First, CompTrack incorporates a Spatial Foreground Predictor (SFP) module to filter out irrelevant background noise based on information entropy, addressing spatial redundancy. Subsequently, its core is an Information Bottleneck-guided Dynamic Token Compression (IB-DTC) module that eliminates the informational redundancy within the foreground. Theoretically grounded in low-rank approximation, this module leverages an online SVD analysis to adaptively compress the redundant foreground into a compact and highly informative set of proxy tokens. Extensive experiments on KITTI, nuScenes and Waymo datasets demonstrate that CompTrack achieves top-performing tracking performance with superior efficiency, running at a real-time 90 FPS on a single RTX 3090 GPU.
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