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arXiv · 2609.16603

G3AR: Graph-Guided Neural Visual Geometry for Scalable Multi-Sequence Aerial Registration

Jeng Wen Joshua Lean·Ting-Yu Yen·Wei-Fang Sun·Simon See·Hung-Kuo Chu·Shih-Hsuan Hung
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Abstract

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 cameras and geometry in a common frame. Across four real aerial scenes, G3AR improves pose error and runtime in matched VGGT- and Pi3-backed comparisons, while its DA3 variant achieves the lowest pose error among evaluated neural-geometry methods.

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Jeng Wen Joshua Lean, Ting-Yu Yen, Wei-Fang Sun, Simon See, Hung-Kuo Chu, Shih-Hsuan Hung. 2026-09-17. G3AR: Graph-Guided Neural Visual Geometry for Scalable Multi-Sequence Aerial Registration. https://doi.org/10.1145/3829339.3847824

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