CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Lee, Seoyoung, Wang, Zhangyang
Institution: The University of Texas at Austin
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7138067232
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