Confidence-Weighted Multiscale Point-to-Plane Change Detection for Point-Cloud-Based Object Monitoring

Repeated three-dimensional point clouds provide a practical basis for monitoring local geometric deformation, but conventional pointwise or single-scale comparison is sensitive to residual registration error, nonuniform sampling, measurement noise, missing observations, and isolated outliers. To address these limitations, this study develops an interpretable confidence-weighted multiscale point-to-plane detector for spatially coherent change in repeated point clouds. Local normals are estimated from covariance matrices at several neighbourhood scales; point-to-plane residuals are fused across scales, modulated by a planarity-density confidence term, thresholded with the median absolute deviation (MAD), and filtered by neighbourhood persistence. The MATLAB implementation was evaluated on a controlled non-planar synthetic surface with Gaussian deformation, rigid displacement, heterogeneous noise, nonuniform sampling, dropout, and outliers. Across 30 independent Monte Carlo realisations, the complete method achieved precision 0.836 ± 0.030, recall 0.967 ± 0.012, F1 0.897 ± 0.018, IoU 0.813 ± 0.029, and FPR 0.0071 ± 0.0018. The single-scale point-to-plane baseline reached F1 0.625 ± 0.021, while confidence-weighted multiscale scoring without persistence reached 0.640 ± 0.019. Trimmed ICP reduced robust nearest-neighbour RMSE from 7.262 ± 0.032 mm to 0.771 ± 0.009 mm. Across tested deformation amplitudes from 3 to 10 mm, F1 increased from 0.749 to 0.930. The results show that spatial persistence provides the largest improvement in false-alarm suppression, with confidence weighting adding a complementary gain. All results are synthetic; sensor-specific detection limits and field accuracy are not claimed.