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: this https URL .

本文介绍了 DOT，一种新颖、简单且高效的方法，用于解决点追踪中存在的遮挡问题，并且通过最近邻插值计算粗糙的初始估计密集流场和可见性掩码，然后利用可学习的光流估计器对其进行精细化处理。实验证明 DOT 相较于当前的光流技术更加准确，在速度方面至少快两个数量级。