Work

Pixel-level image localization for updating 3D digital twins of dams using frequency convolutional networks

Year: 2025

Type: article

Abstract: This paper presents PIL3D, an automated pixel-level image localization framework for maintaining up-to-date 3D digital twins of large-scale civil infrastructure, with a focus on dam structures. Unlike conventional 3D model updating approaches that require extensive manual data acquisition and labor-intensive processing, PIL3D automatically predicts the 3D coordinates of every pixel in an input image relative to an existing model, enabling fully automated dense pixel-to-point correspondences. Experimental validation on a real-world dam case demonstrates centimeter-level localization accuracy, significantly reducing manual intervention, data collection requirements, and computational demand. By integrating PIL3D into digital twin workflows, infrastructure inspection, monitoring, and maintenance can be streamlined into a continuous, automated process, advancing the state of automation in construction and asset management.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106715

Authors: Hang Zhao, Vahidreza Gharehbaghi, Caroline Bennett, Rémy D. Lequesne, Jian Li

Institution: University of Kansas

Language: English

Topic: 3D Surveying and Cultural Heritage

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,490