用视觉方法高效重建涵洞三维结构，减少人工干预。
CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction
- 通过挑选有代表性的图像对，提升视角多样性与匹配精度
- 实现实时同步估计外观、几何和语义信息
- 适用于重复环境下的智能巡检，适合工程维护场景
自动化涵洞检测系统有助于提升防洪管理的安全性和效率。作为该系统的关键步骤，本文提出一种基于RGB的高效3D重建流程，适用于视觉重复性强的涵洞类结构。方法首先使用即插即用模块选择具有代表性的图像对，以最大化视角差异并确保有效对应匹配；随后采用重建模型，实时同步估计RGB外观、几何结构和语义信息。实验表明，该方法能生成高精度的3D重建结果与深度图，在显著提升涵洞检测效率的同时，仅需极少人工干预。
原文摘要 · Abstract (English)
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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