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arXiv · 2603.14150

CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction

Seoyoung Lee·Zhangyang Wang
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Abstract

Automated culvert inspection systems can help increase the safety and efficiency of flood management operations. As a key step to this system, we present an efficient RGB-based 3D reconstruction pipeline for culvert-like structures in visually repetitive environments. Our approach first selects informative frame pairs to maximize viewpoint diversity while ensuring valid correspondence matching using a plug-and-play module, followed by a reconstruction model that simultaneously estimates RGB appearance, geometry, and semantics in real-time. Experiments demonstrate that our method effectively generates accurate 3D reconstructions and depth maps, enhancing culvert inspection efficiency with minimal human intervention.

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Seoyoung Lee, Zhangyang Wang. 2026-03-14. CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction. https://arxiv.org/abs/2603.14150

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