A Study on the Global and Spatial Distribution Evaluation of the Geometric State of Exterior Walls Based on Point Clouds

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Abstract
This study proposes an integrated terrestrial laser scanning (TLS)-based workflow for quantitatively and spatially assessing the relative geometric condition of exterior wall surfaces. The workflow consists of point-cloud acquisition, ROI definition, reference-plane estimation, signed-depth computation, grid-based spatial aggregation, specimen-based validation, and real exterior wall application. Rather than introducing a fundamentally new point-cloud processing algorithm, the main contribution lies in integrating established processing steps into a consistent surface-based assessment procedure and extending deviation evaluation from simple numerical summaries to spatial interpretation. A 3D-printed validation specimen with designed defect depths of 1, 3, 5, and 7 mm was used for quantitative validation. Among 136 designed defects, 123 ground-truth-mapped ROIs were evaluated, resulting in an MAE of 0.795 mm, RMSE of 1.168 mm, and P95 error of 2.511 mm. A RANSAC threshold-based sensitivity analysis confirmed that the final refined reference plane and major signed-depth statistics remained stable within the tested threshold range. The workflow was further applied to a real exterior wall dataset with 29,933,332 strict-ROI points, yielding a mean signed depth of 2.448 mm, median of 2.691 mm, RMSE of 9.956 mm, P95 of 17.121 mm, and maximum value of 90.827 mm. High-deviation regions with an absolute centered signed depth of 15 mm or greater occupied 28.218 m2, corresponding to 10.62% of the valid analysis area, and were distributed across 57 connected clusters. These results indicate that the proposed workflow can support both quantitative deviation assessment and spatial interpretation of high-deviation regions, while the real exterior wall results should be interpreted as a relative geometric assessment and feasibility demonstration rather than absolute accuracy validation or structural damage assessment.
Funding Information
  • Ministry of Land, Infrastructure and Transport (RS-2022-00141900)

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