CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction
This work addresses culvert inspection for flood management, offering an incremental improvement in efficiency with minimal human intervention.
The paper tackles the problem of automated 3D reconstruction for culvert inspection in visually repetitive environments, resulting in an efficient pipeline that generates accurate 3D reconstructions and depth maps in real-time.
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.