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.
@article{arxiv.2603.14150,
title = {CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction},
author = {Seoyoung Lee and Zhangyang Wang},
journal= {arXiv preprint arXiv:2603.14150},
year = {2026}
}