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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01170 |
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| _version_ | 1866911310610956288 |
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| author | Ngo, Tuan Duc Mirzaei, Ashkan Qian, Guocheng Liang, Hanwen Gan, Chuang Kalogerakis, Evangelos Wonka, Peter Wang, Chaoyang |
| author_facet | Ngo, Tuan Duc Mirzaei, Ashkan Qian, Guocheng Liang, Hanwen Gan, Chuang Kalogerakis, Evangelos Wonka, Peter Wang, Chaoyang |
| contents | We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos. Through analysis of existing state-of-the-art methods, we identify two major computational bottlenecks. First, transformer-based iterative tracking becomes expensive when handling a large number of trajectories. To address this, we introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories. The newly added trajectories are initialized using a learnable interpolation module, which is trained end-to-end alongside the tracking network. Second, we propose an optimization that significantly reduces the cost of correlation feature computation, another key bottleneck in prior methods. Together, these improvements lead to a 5-100x speedup over existing approaches while maintaining state-of-the-art tracking accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DELTAv2: Accelerating Dense 3D Tracking Ngo, Tuan Duc Mirzaei, Ashkan Qian, Guocheng Liang, Hanwen Gan, Chuang Kalogerakis, Evangelos Wonka, Peter Wang, Chaoyang Computer Vision and Pattern Recognition We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos. Through analysis of existing state-of-the-art methods, we identify two major computational bottlenecks. First, transformer-based iterative tracking becomes expensive when handling a large number of trajectories. To address this, we introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories. The newly added trajectories are initialized using a learnable interpolation module, which is trained end-to-end alongside the tracking network. Second, we propose an optimization that significantly reduces the cost of correlation feature computation, another key bottleneck in prior methods. Together, these improvements lead to a 5-100x speedup over existing approaches while maintaining state-of-the-art tracking accuracy. |
| title | DELTAv2: Accelerating Dense 3D Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.01170 |