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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03744 |
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| _version_ | 1866912977166270464 |
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| author | Ngo, Tuan Duc Huang, Jiahui Oh, Seoung Wug Blackburn-Matzen, Kevin Kalogerakis, Evangelos Gan, Chuang Lee, Joon-Young |
| author_facet | Ngo, Tuan Duc Huang, Jiahui Oh, Seoung Wug Blackburn-Matzen, Kevin Kalogerakis, Evangelos Gan, Chuang Lee, Joon-Young |
| contents | Estimating accurate, view-consistent geometry and camera poses from uncalibrated multi-view/video inputs remains challenging - especially at high spatial resolutions and over long sequences. We present DAGE, a dual-stream transformer whose main novelty is to disentangle global coherence from fine detail. A low-resolution stream operates on aggressively downsampled frames with alternating frame/global attention to build a view-consistent representation and estimate cameras efficiently, while a high-resolution stream processes the original images per-frame to preserve sharp boundaries and small structures. A lightweight adapter fuses these streams via cross-attention, injecting global context without disturbing the pretrained single-frame pathway. This design scales resolution and clip length independently, supports inputs up to 2K, and maintains practical inference cost. DAGE delivers sharp depth/pointmaps, strong cross-view consistency, and accurate poses, establishing new state-of-the-art results for video geometry estimation and multi-view reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03744 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation Ngo, Tuan Duc Huang, Jiahui Oh, Seoung Wug Blackburn-Matzen, Kevin Kalogerakis, Evangelos Gan, Chuang Lee, Joon-Young Computer Vision and Pattern Recognition Estimating accurate, view-consistent geometry and camera poses from uncalibrated multi-view/video inputs remains challenging - especially at high spatial resolutions and over long sequences. We present DAGE, a dual-stream transformer whose main novelty is to disentangle global coherence from fine detail. A low-resolution stream operates on aggressively downsampled frames with alternating frame/global attention to build a view-consistent representation and estimate cameras efficiently, while a high-resolution stream processes the original images per-frame to preserve sharp boundaries and small structures. A lightweight adapter fuses these streams via cross-attention, injecting global context without disturbing the pretrained single-frame pathway. This design scales resolution and clip length independently, supports inputs up to 2K, and maintains practical inference cost. DAGE delivers sharp depth/pointmaps, strong cross-view consistency, and accurate poses, establishing new state-of-the-art results for video geometry estimation and multi-view reconstruction. |
| title | DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.03744 |