Curvature-Adaptive Smoothing for Multi-View Industrial Metrology

Three-dimensional point cloud data acquired in industrial environments inherently exhibit quality limitations, including measurement noise, local geometric irregularities, and surface roughness. These issues are commonly observed in both structured-light scanning and SIFT-based photogrammetric reconstruction, highlighting the necessity of post-processing in metrology applications where dimensional accuracy and geometric reliability are critical. However, conventional global-parameter smoothing methods, such as Savitzky–Golay filtering, LOWESS, and bilateral filtering, apply uniform smoothing intensity across regions with varying curvature, resulting in an inherent trade-off between noise suppression and geometry preservation. In this study, we propose a learning-independent post-processing framework that adaptively modulates smoothing strength by integrating local curvature estimation with unsupervised anomaly modeling. The proposed approach combines normal-variance-based curvature approximation with k-nearest neighbor anomaly scoring, while CAD data are employed exclusively as an external reference for evaluation. Experimental results on industrial product datasets demonstrate that, for structured-light reconstructions, the proposed method reduces average error to a level comparable to local regression-based smoothing while simultaneously achieving the highest edge-preservation index among all evaluated methods, thereby attaining an optimal operating point between noise suppression and geometric integrity. Although the suppression of extreme deviations remains limited, the geometry-preservation metrics are improved without a significant increase in average error. In contrast, SIFT+COLMAP-based reconstructions exhibit performance comparable to that of the original data, a behavior attributable to low-frequency systematic reconstruction biases rather than high-frequency sensor noise, which fall outside the corrective capacity of purely geometry-driven local smoothing.