Smoothing Algorithm of Point Cloud Based on Normal Vector Correction - XUE Ping; YAO Juan; ZOU Xue-zhou; WANG Hong-min (Journal of Harbin University of Science and Technology)
The presence of outliers and noise points in the cloud data of the reverse engineering data collection directly affects the mult-view’s combination of the data，feature extraction，data reduction and the quality of surface reconstruction. Based on the research of bilateral filtering and trilateration filtering algorithm，this paper presents an algorithm of denoising and smoothing of point cloud data based on normal vector correction. Firstly，the local neighborhood of the point cloud data is constructed，and the noise points of the scattered data collected by the data acquisition system are classified and processed. For outliers in the point cloud data，mathematical statistics analysis is used to filter out the points whose KNN is lower than the threshold. The points with similar geometric characteristics are restricted to the regions where the normal vectors are similar，and the normal vectors and positions of the samples in the similar neighborhoods are triangulated . The improved algorithm can effectively filter the outliers and noise points in the point cloud data，and ensure the sharp and edge features of the point cloud data and obtain good denoising effect.