Radargram-to-point-cloud reconstruction for augmented building inspection workflows
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Abstract
The structural integrity of civil infrastructure necessitates rigorous, regular, and highly accurate inspection methodologies to prevent catastrophic failures and ensure public safety. Among the various non-destructive testing modalities, ground penetrating radar has emerged as a premier technology for subsurface investigation. However, the interpretation of standard two-dimensional radargrams is notoriously complex, requiring highly trained personnel and often resulting in cognitive overload during on-site inspections. This paper presents a comprehensive framework for radargram-to-point-cloud reconstruction specifically designed to enhance augmented building inspection workflows. By transforming raw electromagnetic echo data into rich three-dimensional point clouds, the proposed system bridges the semantic gap between abstract radar signals and physical building structures. The methodology encompasses advanced signal preprocessing, automated hyperbola detection, spatial mapping through coordinate transformation, and point cloud refinement. Furthermore, the integration of these reconstructed point clouds into augmented reality environments facilitates intuitive, real-time visualization of embedded structural elements such as reinforcement bars, conduits, and hidden defects. Extensive experimental evaluations conducted on reinforced concrete testbeds demonstrate the efficacy, spatial accuracy, and computational efficiency of the proposed pipeline. The results indicate significant improvements in defect localization and user interpretation speeds, establishing a robust foundation for next-generation interactive structural health monitoring systems.
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