MV-TAP: Tracking Any Point in Multi-View Videos
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909938628952064 |
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| author | Koo, Jahyeok Kim, Inès Hyeonsu Kim, Mungyeom Park, Junghyun Park, Seohyun Kim, Jaeyeong Yi, Jung Cho, Seokju Kim, Seungryong |
| author_facet | Koo, Jahyeok Kim, Inès Hyeonsu Kim, Mungyeom Park, Junghyun Park, Seohyun Kim, Jaeyeong Yi, Jung Cho, Seokju Kim, Seungryong |
| contents | Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications. In this work, we present MV-TAP, a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-view information. MV-TAP utilizes camera geometry and a cross-view attention mechanism to aggregate spatio-temporal information across views, enabling more complete and reliable trajectory estimation in multi-view videos. To support this task, we construct a large-scale synthetic training dataset and real-world evaluation sets tailored for multi-view tracking. Extensive experiments demonstrate that MV-TAP outperforms existing point-tracking methods on challenging benchmarks, establishing an effective baseline for advancing research in multi-view point tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MV-TAP: Tracking Any Point in Multi-View Videos Koo, Jahyeok Kim, Inès Hyeonsu Kim, Mungyeom Park, Junghyun Park, Seohyun Kim, Jaeyeong Yi, Jung Cho, Seokju Kim, Seungryong Computer Vision and Pattern Recognition Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications. In this work, we present MV-TAP, a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-view information. MV-TAP utilizes camera geometry and a cross-view attention mechanism to aggregate spatio-temporal information across views, enabling more complete and reliable trajectory estimation in multi-view videos. To support this task, we construct a large-scale synthetic training dataset and real-world evaluation sets tailored for multi-view tracking. Extensive experiments demonstrate that MV-TAP outperforms existing point-tracking methods on challenging benchmarks, establishing an effective baseline for advancing research in multi-view point tracking. |
| title | MV-TAP: Tracking Any Point in Multi-View Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.02006 |