Feature preserving consolidation for unorganized point clouds

We introduce a novel method for the consolidation of unorganized point clouds with noise, outliers, non-uniformities as well as sharp features. This method is feature preserving, in the sense that given an initial estimation of normal, it is able to recover the sharp features contained in the original geometric data which are usually contaminated during the acquisition. The key ingredient of our approach is a weighting term from normal space as an effective complement to the recently proposed consolidation techniques. Moreover, a normal mollification step is employed during the consolidation to get normal information respecting sharp features besides the position of each point. Experiments on both synthetic and real-world scanned models validate the ability of our approach in producing denoised, evenly distributed and feature preserving point clouds, which are preferred by most surface reconstruction methods.