Implicit surface reconstruction from noisy 3D scattered data

The paper presents a method for surface reconstruction from large unorganized and noisy point sets without any normal or orientation information. Firstly, the outliers will be selected and deleted, and acquire new point sets being less noisy with improved method of fuzzy c-means clustering. Secondly, we compute the normal of the new point sets through PCA analysis. Then we interpolate single-level and multi-level the new points acquired by clustering method by Compactly Supported Radial Basis Function (CSRBF). The experiments show that the outliers will be selected well and the noisy point sets will be smoothed. The interpolation points are renewed by clustering method, and implicit reconstruction by CSRBF may provide a solution for surface reconstruction from noisy and incomplete data. In our algorithm, we represent the point sets by an octree-based subdivision of the bounding box to reduce computation complexity.