Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data
Two statistical techniques are proposed for outlier detection in point cloud data. The proposed methods can fit robust plane in laser scanning data. The proposed methods produce robust normal and curvature in point cloud processing. They are faster and robust than RANSAC, robust PCA and other existing efficient methods. They have potential for point cloud denoising, segmentation, and reconstruction.
point cloud denoising segmentation feature extraction plane fitting robust saliency feature surface reconstruction