MVSAnywhere: Zero-Shot Multi-View Stereo
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909556087455744 |
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| author | Izquierdo, Sergio Sayed, Mohamed Firman, Michael Garcia-Hernando, Guillermo Turmukhambetov, Daniyar Civera, Javier Mac Aodha, Oisin Brostow, Gabriel Watson, Jamie |
| author_facet | Izquierdo, Sergio Sayed, Mohamed Firman, Michael Garcia-Hernando, Guillermo Turmukhambetov, Daniyar Civera, Javier Mac Aodha, Oisin Brostow, Gabriel Watson, Jamie |
| contents | Computing accurate depth from multiple views is a fundamental and longstanding challenge in computer vision. However, most existing approaches do not generalize well across different domains and scene types (e.g. indoor vs. outdoor). Training a general-purpose multi-view stereo model is challenging and raises several questions, e.g. how to best make use of transformer-based architectures, how to incorporate additional metadata when there is a variable number of input views, and how to estimate the range of valid depths which can vary considerably across different scenes and is typically not known a priori? To address these issues, we introduce MVSA, a novel and versatile Multi-View Stereo architecture that aims to work Anywhere by generalizing across diverse domains and depth ranges. MVSA combines monocular and multi-view cues with an adaptive cost volume to deal with scale-related issues. We demonstrate state-of-the-art zero-shot depth estimation on the Robust Multi-View Depth Benchmark, surpassing existing multi-view stereo and monocular baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MVSAnywhere: Zero-Shot Multi-View Stereo Izquierdo, Sergio Sayed, Mohamed Firman, Michael Garcia-Hernando, Guillermo Turmukhambetov, Daniyar Civera, Javier Mac Aodha, Oisin Brostow, Gabriel Watson, Jamie Computer Vision and Pattern Recognition Computing accurate depth from multiple views is a fundamental and longstanding challenge in computer vision. However, most existing approaches do not generalize well across different domains and scene types (e.g. indoor vs. outdoor). Training a general-purpose multi-view stereo model is challenging and raises several questions, e.g. how to best make use of transformer-based architectures, how to incorporate additional metadata when there is a variable number of input views, and how to estimate the range of valid depths which can vary considerably across different scenes and is typically not known a priori? To address these issues, we introduce MVSA, a novel and versatile Multi-View Stereo architecture that aims to work Anywhere by generalizing across diverse domains and depth ranges. MVSA combines monocular and multi-view cues with an adaptive cost volume to deal with scale-related issues. We demonstrate state-of-the-art zero-shot depth estimation on the Robust Multi-View Depth Benchmark, surpassing existing multi-view stereo and monocular baselines. |
| title | MVSAnywhere: Zero-Shot Multi-View Stereo |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.22430 |