MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their reliance on full attention makes them fundamentally limited by GPU memory capacity, preventing them from scaling to large, unordered image collections. We introduce MERG3R, a training-free divide-and-conquer framework that enables geometric foundation models to operate far beyond their native memory limits. MERG3R first reorders and partitions unordered images into overlapping, geometrically diverse subsets that can be reconstructed independently. It then merges the resulting local reconstructions through an ef...
| Authors | Leo Kaixuan Cheng, Abdus Shaikh, Ruofan Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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