Point cloud denoising using robust principal component analysis

This paper presents a new method for filtering noise occurring in point cloud sampled data. The method smoothes the data set whereas preserves sharp features. We propose a new weighted variant of the principal component analysis method, which instead of using exponential weighting factors inversely proportional to the Euclidean distance to the mean, which is computationally expensive, uses weighting factors assignment by inversely proportional repartition of the sum of distance to the mean. The determination of weighted factors by means of inverse proportional repartition makes our variant robust to outliers. Additionally, we propose a simple solution to the problem of data shrinkage produced by the linear local fitting of the principal component analysis. The proposed method is simple, easy to implement, and effective for noise filtering.