Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views

2026-05-08

AI Summary

Existing 3D vision systems rely on spatial and appearance overlap between views, making them ill-suited for reconstruction tasks under non-overlapping viewpoints—such as those encountered in distributed robotic systems or crowdsourced data collection. This work formally defines and addresses, for the first time, the problem of non-overlapping multi-view 3D reconstruction by introducing GLADOS, an architecture-agnostic framework comprising three stages: generative bridging, robust coarse reconstruction, and iterative contextual refinement. GLADOS leverages foundation models to synthesize intermediate views, performs globally aligned coarse reconstruction, and iteratively expands and optimizes geometry with consistency constraints to achieve geometrically complete and semantically coherent reconstructions. Evaluated on a newly curated benchmark dataset, GLADOS significantly outperforms existing methods, effectively mitigating geometric fragmentation and semantic inconsistency, thereby offering the first viable solution for zero-overlap reconstruction scenarios.

Abstract

3D vision systems are fundamentally constrained by their reliance on visual overlap: reconstruction methods require it for geometric alignment, while generative models use it to enforce multi-view consistency. This limitation is particularly acute in real-world scenarios such as distributed swarm robotics or crowd-sourced data collection, where capturing overlapping perspectives, both in terms of spatial and appearance overlap, is often impossible. We introduce Generative Reconstruction from Disjoint Views as a new paradigm, establish a comprehensive dataset, and propose specialized evaluation metrics for zero-overlap scenarios. Our benchmarking demonstrates that existing state-of-the-art methods fail catastrophically on this task, producing disconnected geometries or semantically incoherent reconstructions. To address these limitations, we propose GLADOS, a general, modular framework that operates through three stages: (1) Generative Bridging, where foundation models synthesize intermediate perspectives to connect disjoint inputs; (2) Robust Coarse 3D Reconstruction, that establish coarse geometric scaffold via global alignment which absorbs local contradictions from generative process; and (3) Iterative Context Expansion and Consistency Optimization to fill missing regions and unify the reconstruction. As an architectureagnostic framework, GLADOS enables seamless integration of future advances in generation, reconstruction, and inpainting. The source code is available at: https://github.com/gwilczynski95/GLADOS.

Problem

• disjoint views
• 3D reconstruction
• multi-view consistency
• zero-overlap
• generative modeling

Innovation

• Disjoint View Reconstruction
• Generative Bridging
• Zero-Overlap 3D Vision
• Modular 3D Framework
• GLADOS