G3AR: Graph-Guided Neural Visual Geometry for Scalable Multi-Sequence Aerial Registration
Full-context neural visual geometry is impractical for thousands of images, while sequence-based chunking poorly captures irregular non-local overlap in multi-sequence aerial collections. We present Graph-Guided Neural Visual Geometry for Aerial Registration (G3AR), a graph-guided framework for scalable dense neural geometry. Before local inference, G3AR builds a geometrically verified image-proximity graph that guides bounded overlapping chunks and induces a chunk graph whose maximum spanning tree defines alignment topology. Compatible backbones process chunks independently; shared-image predictions then estimate three-dimensional similarity (Sim(3)) transforms that register local camera...
| Authors | Jeng Wen Joshua Lean, Ting-Yu Yen, Wei-Fang Sun et al. |
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| Primary category | cs.CV |
| Published | 15 September 2026 |
| Last updated at source | 17 September 2026 |
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