密集光流跟踪：连接各个点
Dense Optical Tracking: Connecting the Dots
AI总结：
本文提出 DOT，通过少量关键轨迹插值并由可学习光流估计器细化，实现快速密集点跟踪，精度媲美或超过 CoTracker 且速度至少快百倍。
摘要：
近期的点跟踪方法能够在存在遮挡的情况下，恢复视频中大部分片段内任意场景点的轨迹。然而，在实际应用中，它们速度过慢，无法在合理时间内跟踪单帧中观察到的每个点。本文介绍了 DOT，这是一种新颖、简单且高效的方法，用于解决该问题。该方法首先使用现成的点跟踪算法，从运动边界处的关键区域提取少量轨迹。给定源帧和目标帧后，DOT 通过最近邻插值计算密集光流场和可见性掩码的粗略初始估计，随后使用一个可学习的光流估计器对其进行细化；该估计器能够显式处理遮挡，并可在具有真值对应关系的合成数据上训练。我们表明，DOT 显著比当前光流技术更准确，优于 OmniMotion 等复杂的“通用”跟踪器，并且与 CoTracker 等最佳点跟踪算法相当甚至更好，同时速度至少快两个数量级。在合成视频和真实视频上的定量与定性实验验证了所提方法的前景。展示本方法能力的代码、数据和视频可在项目网页获取：https://16lemoing.github.io/dot 。
英文摘要：
Recent approaches to point tracking are able to recover the trajectory of any scene point through a large portion of a video despite the presence of occlusions. They are, however, too slow in practice to track every point observed in a single frame in a reasonable amount of time. This paper introduces DOT, a novel, simple and efficient method for solving this problem. It first extracts a small set of tracks from key regions at motion boundaries using an off-the-shelf point tracking algorithm. Given source and target frames, DOT then computes rough initial estimates of a dense flow field and visibility mask through nearest-neighbor interpolation, before refining them using a learnable optical flow estimator that explicitly handles occlusions and can be trained on synthetic data with ground-truth correspondences. We show that DOT is significantly more accurate than current optical flow techniques, outperforms sophisticated "universal" trackers like OmniMotion, and is on par with, or better than, the best point tracking algorithms like CoTracker while being at least two orders of magnitude faster. Quantitative and qualitative experiments with synthetic and real videos validate the promise of the proposed approach. Code, data, and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing.github.io/dot .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
