3D Sonar Point Cloud Denoising Constrained by Local Spatial Features and Global Region Growth Algorithm

Three-dimensional (3D) sonar overcomes the limitations of traditional measurement methods regarding imaging coverage and accuracy, making it indispensable for underwater structure monitoring. However, complex underwater environments often introduce significant noise into 3D sonar data, degrading monitoring performance. To address this, we propose a geometry-based filtering method. First, Total Least Squares (TLS) is employed to construct local spatial features, which guides a region-growing segmentation based on normal vector attributes. Subsequently, the resulting clusters are refined using these local geometric characteristics. Finally, statistical filtering is applied to eliminate residual outliers from a local to a global scale. Experimental results demonstrate that the proposed method achieves F1 scores of 78.65% and 84.49% in outlier removal, effectively suppressing noise while preserving structural integrity.