通过分阶段追踪与特征优化，让3D点跟踪快5到100倍。
DELTAv2: Accelerating Dense 3D Tracking
- 先用少量点粗略追踪，逐步扩展并用可学习插值初始化新轨迹。
- 减少相关特征计算开销，整体速度提升5至100倍。
- 适合需要高效长时3D点跟踪的视觉系统开发者。
我们提出一种新算法，用于加速视频中密集长时3D点追踪。通过对现有先进方法的分析，识别出两大计算瓶颈：一是基于Transformer的迭代追踪在处理大量轨迹时成本高昂；为此，我们引入粗到精策略，从少量点开始追踪，并逐步扩展轨迹集，新增轨迹由端到端训练的可学习插值模块初始化。二是提出优化显著降低相关特征计算开销，这是以往方法的关键瓶颈。两项改进结合后，相较现有方法实现5至100倍的速度提升，同时保持最先进的追踪精度。
原文摘要 · Abstract (English)
We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos. Through analysis of existing state-of-the-art methods, we identify two major computational bottlenecks. First, transformer-based iterative tracking becomes expensive when handling a large number of trajectories. To address this, we introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories. The newly added trajectories are initialized using a learnable interpolation module, which is trained end-to-end alongside the tracking network. Second, we propose an optimization that significantly reduces the cost of correlation feature computation, another key bottleneck in prior methods. Together, these improvements lead to a 5-100x speedup over existing approaches while maintaining state-of-the-art tracking accuracy.
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