Robust mesh reconstruction from unoriented noisy points

We present a robust method to generate mesh surfaces from unoriented noisy points in this paper. The whole procedure consists of three steps. Firstly, the normal vectors at points are evaluated by a highly robust estimator which can fit surface corresponding to less than half of the data points and fit data with multi-structures. This benefits us with the ability to well reconstruct the normal vectors around sharp edges and corners. Meanwhile, clean point cloud equipped with piecewise normal is obtained by projecting points according to the robust fitting. Secondly, an error-minimized subsampling is applied to generate a well-sampled point cloud. Thirdly, a combinatorial approach is employed to reconstruct a triangular mesh connecting the down-sampled points, and a polygonal mesh which preserves sharp features is constructed by the dual-graph of triangular mesh. Parallelization method of the algorithm on a consumer PC using the architecture of GPU is also given.