ReTracker: Exploring Image Matching for Robust Online Any Point Tracking

This paper aims to establish correspondences for a set of 2D query points across a video sequence in an online manner. Recent methods leverage future frames to achieve smooth point tracking at the current frame, but they still struggle to find points with significant viewpoint changes after long-term occlusions and inherently cannot achieve online tracking. To overcome these challenges, we develop a novel online tracking framework, named ReTracker, that integrates two advances in image matching with tracking-specific designs. First, a decoder network with a global receptive field is incorporated with a temporal attention module to robustly track points undergoing large location changes. Second, the decoder network is adapted to pretrain on large-scale two-view matching data, which offers significantly greater diversity and volume than tracking data, to learn general matching priors. This pretraining strategy effectively enhances our tracker's ability to handle viewpoint and appearance variations after long-term occlusions. Experiments demonstrate that our method outperforms recent online trackers across multiple benchmarks and achieves competitive or superior performance compared to offline methods. Furthermore, we collect an ego-centric, occlusion-heavy dataset to illustrate the retracking capabilities of our approach. The code and dataset will be released for the reproducibility.