Paparazzo: Active Mapping of Moving 3D Objects
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914495321866240 |
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| author | Allegro, Davide Li, Shiyao Ghidoni, Stefano Lepetit, Vincent |
| author_facet | Allegro, Davide Li, Shiyao Ghidoni, Stefano Lepetit, Vincent |
| contents | Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To address this limitation, we introduce the novel task of active mapping of moving objects, in which a mapping agent must plan its trajectory while compensating for the object's motion. Our approach, Paparazzo, provides a learning-free solution that robustly predicts the target's trajectory and identifies the most informative viewpoints from which to observe it, to plan its own path. We also contribute a comprehensive benchmark designed for this new task. Through extensive experiments, we show that Paparazzo significantly improves 3D reconstruction completeness and accuracy compared to several strong baselines, marking an important step toward dynamic scene understanding. Project page: https://davidea97.github.io/paparazzo-page/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19556 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Paparazzo: Active Mapping of Moving 3D Objects Allegro, Davide Li, Shiyao Ghidoni, Stefano Lepetit, Vincent Computer Vision and Pattern Recognition Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To address this limitation, we introduce the novel task of active mapping of moving objects, in which a mapping agent must plan its trajectory while compensating for the object's motion. Our approach, Paparazzo, provides a learning-free solution that robustly predicts the target's trajectory and identifies the most informative viewpoints from which to observe it, to plan its own path. We also contribute a comprehensive benchmark designed for this new task. Through extensive experiments, we show that Paparazzo significantly improves 3D reconstruction completeness and accuracy compared to several strong baselines, marking an important step toward dynamic scene understanding. Project page: https://davidea97.github.io/paparazzo-page/ |
| title | Paparazzo: Active Mapping of Moving 3D Objects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.19556 |