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. While point tracking in two dimensions (TAP) has many benchmarks measuring performance on real-world videos%2C such as TAPVid-DAVIS%2C three-dimensional point tracking has none. To this end%2C leveraging existing footage%2C we build a new benchmark for 3D point tracking featuring 4%2C000%2B real-world videos%2C composed of three different data sources spanning a variety of object types%2C motion patterns%2C and indoor and outdoor environments. To measure performance on the TAP-3D task%2C we formulate a collection of metrics that extend the Jaccard-based metric used in TAP to handle the complexities of ambiguous depth scales across models%2C occlusions%2C and multi-track spatio-temporal smoothness. We manually verify a large sample of trajectories to ensure correct video annotations%2C and assess the current state of the TAP-3D task by constructing competitive baselines using existing tracking models. We anticipate this benchmark will serve as a guidepost to improve our ability to understand precise 3D motion and surface deformation from monocular video. Code for dataset download%2C generation%2C and model evaluation is available at https:%2F%2Ftapvid3d.github.io?quality=80&w=800)
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