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

Seoyoung Lee, Zhangyang Wang

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.

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

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.
Paper Structure (7 sections, 2 equations, 3 figures, 2 tables)

This paper contains 7 sections, 2 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Informative Pairwise Frame Selection
  4. Semantic 3D Reconstruction
  5. Experiments & Results
  6. Limitations & Future Works
  7. Conclusion

Figures (3)

  • Figure 1: Camera pose alignment. For each scene, ground truth poses were scaled and aligned to match the coordinate system predicted by the reconstruction model.
  • Figure 2: Application of pairwise selection algorithm to LSM. For each scene, two informative frames are selected as input to the reconstruction model, which simultaneously predicts RGB appearance, depth map, and feature field for a smooth interpolated trajectory that includes novel views.
  • Figure 3: Application of pairwise selection algorithm to Baseline models. The pairwise selection algorithm can help to increase the potential for successful scene reconstruction of novel views for various models in general.