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原文摘要

Existing 3D point tracking methods mostly rely on heuristic designs or scene reconstruction, which incurs significant computational overhead and makes it difficult to meet the demands of real-time applications. To address this problem, in this work, we present a novel spatial tracker that leverages a feed-forward visual geometry transformer to predict the trajectories of arbitrary query points from monocular videos in real time. Specifically, we employ a query initialization mechanism to maintain and update a global feature vector and a set of frame-level feature vectors for each query point. Then, we propose a new spatial tracking framework, which consists of a visual geometry transformer backbone, a global embedding branch, a frame-level embedding branch, and a tracking head. The key innovation lies in the dual-branch embedding design, where the global embedding branch integrates geometry-grounded features of the entire video into global query features to optimize track information across the entire sequence and the frame-level branch combines geometry-grounded features of each respective frame into frame-level query features to refine fine-grained track coordinate predictions. Furthermore, to facilitate collaboration between the global branch and the frame-level branch, we introduce an interaction module which enables unidirectional or bidirectional information exchange between the global query features and frame-level query features. Extensive experiments on various point tracking benchmark datasets show that our approach achieves significantly fast spatial tracking speed compared with state-of-the-art methods, while maintaining comparable tracking accuracy.