MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry

• URL: http://arxiv.org/abs/2603.02351v1
• Date: Mon, 02 Mar 2026 19:49:25 GMT
• Title: MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
• Authors: Leo Kaixuan Cheng, Abdus Shaikh, Ruofan Liang, Zhijie Wu, Yushi Guan, Nandita Vijaykumar,
• Abstract summary: We introduce MERG3R, a training-free divide-and-conquer framework for geometric foundation models.<n> MERG3R partitions unordered images into overlapping, geometrically diverse subsets that can be reconstructed independently.<n>It then merges the resulting local reconstructions through an efficient global alignment and confidence-weighted bundle adjustment procedure.<n>Across large-scale datasets, including 7-Scenes, NRGBD, Tanks & Temples, and Cambridge Landmarks, MERG3R consistently improves reconstruction accuracy, memory efficiency, and scalability.
• Score: 6.060187129166582
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 efficient global alignment and confidence-weighted bundle adjustment procedure, producing a globally consistent 3D model. Our framework is model-agnostic and can be paired with existing neural geometry models. Across large-scale datasets, including 7-Scenes, NRGBD, Tanks & Temples, and Cambridge Landmarks, MERG3R consistently improves reconstruction accuracy, memory efficiency, and scalability, enabling high-quality reconstruction when the dataset exceeds memory capacity limits.

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