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

• URL: http://arxiv.org/abs/2603.14150v1
• Date: Sat, 14 Mar 2026 23:00:19 GMT
• Title: CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction
• Abstract summary: We present an efficient RGB-based 3D reconstruction pipeline for culvert-like structures in visually repetitive environments.<n>Our approach first selects informative frame pairs to maximize viewpoint diversity while ensuring valid correspondence matching.<n> Experiments demonstrate that our method effectively generates accurate 3D reconstructions and depth maps, enhancing culvert inspection efficiency with minimal human intervention.
• Score: 53.9319127526742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• DINOcular: Self-Supervised Visuospatial Representations [7.360872501623849]
  We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations.<n>Our method integrates depth-derived geometric priors with a visual backbone through inter-patch and intra-patch fusion.<n>The resulting representation shows promising improvements on 3D awareness while preserving semantic transfer.
  arXiv  Detail & Related papers  (2026-08-27T15:09:32Z)
• PoInit-of-View: Poisoning Initialization of Views Transfers Across Multiple 3D Reconstruction Systems [55.84012796735184]
  Poisoning input views of 3D reconstruction systems has been recently studied.<n>We propose PoInit-of-View, which optimize adversarial perturbations to intentionally introduce cross-view inconsistencies.<n> Experimental results demonstrate the effectiveness of our PoInit-of-View on diverse 3D reconstruction systems and datasets.
  arXiv  Detail & Related papers  (2026-04-17T00:48:12Z)
• Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction [53.26903617819014]
  Flow-Matching-guided Unfolding network (FMU) is first to integrate flow matching into HSI reconstruction.<n>To further strengthen the learned dynamics, we introduce a mean velocity loss.<n>Experiments on both simulated and real datasets show that FMU significantly outperforms existing approaches in reconstruction quality.
  arXiv  Detail & Related papers  (2025-10-02T11:32:00Z)
• Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline [64.42938561167402]
  We propose an online 3D reconstruction method using 3D Gaussian-based SLAM, combined with a feed-forward recurrent prediction module.<n>This approach replaces slow test-time optimization with fast network inference, significantly improving tracking speed.<n>Our method achieves performance on par with the state-of-the-art SplaTAM, while reducing tracking time by more than 90%.
  arXiv  Detail & Related papers  (2025-08-06T16:16:58Z)
• Surf3R: Rapid Surface Reconstruction from Sparse RGB Views in Seconds [34.38496869014632]
  Surf3R is an end-to-end feedforward approach that reconstructs 3D surfaces from sparse views without estimating camera poses.<n>Our method employs a multi-branch and multi-view decoding architecture in which multiple reference views jointly guide the reconstruction process.
  arXiv  Detail & Related papers  (2025-08-06T14:53:42Z)
• GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity [49.31257173003408]
  We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video.<n>Our approach demonstrates strong capabilities in recovering high-fidelity object meshes, setting a new standard for single-sensor 3D reconstruction in open-world environments.
  arXiv  Detail & Related papers  (2025-05-17T08:46:29Z)
• FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction [50.534213038479926]
  FreeSplat++ is an alternative approach to large-scale indoor whole-scene reconstruction.<n>Our method with depth-regularized per-scene fine-tuning demonstrates substantial improvements in reconstruction accuracy and a notable reduction in training time.
  arXiv  Detail & Related papers  (2025-03-29T06:22:08Z)
• Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization [27.509109317973817]
  3D Gaussian Splatting (3DGS) has garnered significant attention for its high-quality rendering and fast inference speed.<n>Previous methods primarily focus on geometry regularization, with common approaches including primitive-based and dual-model frameworks.<n>We propose CarGS, a unified model leveraging-adaptive regularization to achieve simultaneous, high-quality surface reconstruction.
  arXiv  Detail & Related papers  (2025-03-02T12:51:38Z)
• BS3D: Building-scale 3D Reconstruction from RGB-D Images [25.604775584883413]
  We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms.
  arXiv  Detail & Related papers  (2023-01-03T11:46:14Z)
• Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation [111.89519571205778]
  In this work, we propose an alternative domain-adaptive approach to depth estimation. Our novel two-step structure first trains a depth estimation network with labeled synthetic images in a supervised manner. The results of our experiments show that the proposed method improves the network's performance on real images by a considerable margin.
  arXiv  Detail & Related papers  (2021-09-24T08:11:34Z)
• VIO-Aided Structure from Motion Under Challenging Environments [12.111638631118026]
  We present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs.
  arXiv  Detail & Related papers  (2021-01-24T06:35:52Z)
• Monocular Real-Time Volumetric Performance Capture [28.481131687883256]
  We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video. Our system reconstructs a fully textured 3D human from each frame by leveraging Pixel-Aligned Implicit Function (PIFu) We also introduce an Online Hard Example Mining (OHEM) technique that effectively suppresses failure modes due to the rare occurrence of challenging examples.
  arXiv  Detail & Related papers  (2020-07-28T04:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.