3D point cloud global denoising algorithm based on different noise characteristics

Abstract

Abstract When acquiring object point cloud data by three-dimensional scanning technology, noise is generated due to instrument accuracy and external factors. Existing algorithms rarely consider the characteristics of different noises and different regional noises when solving the point cloud denoising problem, this results in a limited effect on denoising. This paper presents an algorithm for denoising based on the characteristics of different types of noise and different regions in the point cloud. The algorithm includes large-scale noise removal and small-scale noise smoothing. Remove large-scale noise points by the relationship between the local point cloud and the global point cloud. For small-scale noise, the feature regions and non-feature regions are extracted according to the normal cosine information entropy. According to the characteristics of the small-scale noise in two regions, the noise distance distribution and the optimized bilateral filtering are used to deal with the small-scale noise in two regions respectively. Comparison experiments show that our algorithm can effectively remove the noise points mixed in the normal point cloud. The accuracy of large-scale noise removal reaches 99.1%. The proposed algorithm can protect the feature areas from being over-smoothed while smoothing the small-scale noise in non-featured areas.