{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tapvid-3d-a-benchmark-for-tracking-any-point","title":"TAPVid-3D: A Benchmark for Tracking Any Point in 3D","arxiv_id":"2407.05921","date":"2024-07-08","proceeding":null,"authors":["Skanda Koppula","Ignacio Rocco","Yi Yang","Joe Heyward","João Carreira","Andrew Zisserman","Gabriel Brostow","Carl Doersch"],"abstract":"We introduce a new benchmark, TAPVid-3D, for evaluating the task of long-range Tracking Any Point in 3D (TAP-3D). While point tracking in two dimensions (TAP) has many benchmarks measuring performance on real-world videos, such as TAPVid-DAVIS, three-dimensional point tracking has none. To this end, leveraging existing footage, we build a new benchmark for 3D point tracking featuring 4,000+ real-world videos, composed of three different data sources spanning a variety of object types, motion patterns, and indoor and outdoor environments. To measure performance on the TAP-3D task, 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, occlusions, and multi-track spatio-temporal smoothness. We manually verify a large sample of trajectories to ensure correct video annotations, 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, generation, and model evaluation is available at https://tapvid3d.github.io","url_abs":"https://arxiv.org/abs/2407.05921v2","url_pdf":"https://arxiv.org/pdf/2407.05921v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tapvid-3d-a-benchmark-for-tracking-any-point","repo_url":"https://github.com/google-deepmind/tapnet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"tapvid-3d-a-benchmark-for-tracking-any-point","repo_url":"https://github.com/deepmind/tapnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"point-tracking","task_name":"Point Tracking"}],"methods":[],"datasets_introduced":[{"slug":"tapvid-3d-a-benchmark-for-tracking-any-point","name":"TAPVid-3D: A Benchmark for Tracking Any Point in 3D","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2407.05921","atlas_url":"https://app.syntology.ai/?focus=2407.05921","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}