Look Around and Pay Attention: Multi-camera Point Tracking Reimagined with Transformers
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918230861283328 |
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| author | Galoaa, Bishoy Bai, Xiangyu Moezzi, Shayda Nandi, Utsav Rangoju, Sai Siddhartha Vivek Dhir Amraee, Somaieh Ostadabbas, Sarah |
| author_facet | Galoaa, Bishoy Bai, Xiangyu Moezzi, Shayda Nandi, Utsav Rangoju, Sai Siddhartha Vivek Dhir Amraee, Somaieh Ostadabbas, Sarah |
| contents | This paper presents LAPA (Look Around and Pay Attention), a novel end-to-end transformer-based architecture for multi-camera point tracking that integrates appearance-based matching with geometric constraints. Traditional pipelines decouple detection, association, and tracking, leading to error propagation and temporal inconsistency in challenging scenarios. LAPA addresses these limitations by leveraging attention mechanisms to jointly reason across views and time, establishing soft correspondences through a cross-view attention mechanism enhanced with geometric priors. Instead of relying on classical triangulation, we construct 3D point representations via attention-weighted aggregation, inherently accommodating uncertainty and partial observations. Temporal consistency is further maintained through a transformer decoder that models long-range dependencies, preserving identities through extended occlusions. Extensive experiments on challenging datasets, including our newly created multi-camera (MC) versions of TAPVid-3D panoptic and PointOdyssey, demonstrate that our unified approach significantly outperforms existing methods, achieving 37.5% APD on TAPVid-3D-MC and 90.3% APD on PointOdyssey-MC, particularly excelling in scenarios with complex motions and occlusions. Code is available at https://github.com/ostadabbas/Look-Around-and-Pay-Attention-LAPA- |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Look Around and Pay Attention: Multi-camera Point Tracking Reimagined with Transformers Galoaa, Bishoy Bai, Xiangyu Moezzi, Shayda Nandi, Utsav Rangoju, Sai Siddhartha Vivek Dhir Amraee, Somaieh Ostadabbas, Sarah Computer Vision and Pattern Recognition This paper presents LAPA (Look Around and Pay Attention), a novel end-to-end transformer-based architecture for multi-camera point tracking that integrates appearance-based matching with geometric constraints. Traditional pipelines decouple detection, association, and tracking, leading to error propagation and temporal inconsistency in challenging scenarios. LAPA addresses these limitations by leveraging attention mechanisms to jointly reason across views and time, establishing soft correspondences through a cross-view attention mechanism enhanced with geometric priors. Instead of relying on classical triangulation, we construct 3D point representations via attention-weighted aggregation, inherently accommodating uncertainty and partial observations. Temporal consistency is further maintained through a transformer decoder that models long-range dependencies, preserving identities through extended occlusions. Extensive experiments on challenging datasets, including our newly created multi-camera (MC) versions of TAPVid-3D panoptic and PointOdyssey, demonstrate that our unified approach significantly outperforms existing methods, achieving 37.5% APD on TAPVid-3D-MC and 90.3% APD on PointOdyssey-MC, particularly excelling in scenarios with complex motions and occlusions. Code is available at https://github.com/ostadabbas/Look-Around-and-Pay-Attention-LAPA- |
| title | Look Around and Pay Attention: Multi-camera Point Tracking Reimagined with Transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04213 |