Denoising image point clouds using segmentation and synthetic data for enhanced structural health analysis of tunnels

Abstract

Abstract Point clouds derived from UAV photogrammetry are a cost-effective alternative to LiDAR for infrastructure inspections, but they often include both structural and non-structural elements that complicate analysis. Traditional denoising filters remove outliers indiscriminately and frequently erode edges, making it difficult to preserve the curved tunnel lining while distinguishing bolts, access gates, or pipelines. In contrast, segmentation-based approaches leverage geometric context to explicitly separate lining surfaces from ancillary components, thereby enabling more accurate deformation analysis and structural assessment. To that end, this paper presents a novel approach for denoising image point clouds using a synthetic training dataset to address the scarcity of labeled public data for enhancing point cloud quality. Unlike other denoising approaches that rely on projections or assume points lie on a predefined surface shape, this segmentation-based denoising method retains only meaningful points in their original locations, allowing for more accurate analysis of deformation. Enhanced by synthetic training datasets, the application of the proposed denoising method to a road tunnel image point cloud and a subway tunnel terrestrial laser scanning point cloud demonstrates its potential to enhance point cloud quality in tunnels with diverse geometries and point cloud data resources, even when data are limited. The method achieves an 80% mean intersection over union for both the road tunnel and the subway tunnel from manual annotation. This enables an improvement in structural deformation analysis at the mm level.