Point Model Smoothing Denoising And Resampling

Point sets are receiving a growing amount of attention as a representation of models in computer graphics. The emergence of affordable and accurate scanning devices and its simple data structure drive the point sets move forward. Point sets are being applied on many fields as medical, engineering, topography, simulation, game & movie, E-commerce and art-history. The Thesis presents two new algorithms on denosing and up-sampling on point sets.Different from other local denoising algorithms on 3d shapes, we proposed a non-local (n1) denoising algorithm which focuses on the global geometry feature distribution. According to the statistic of the noisy model, the algorithm can distinguish the feature and noise automatically. Feature preservation and anti-shrinkage are posed by our algorithm.Our novel up-sampling algorithm aimed at holes-covering of point sets. We firstly find the boundary of the holes, and then resample new points near the boundary points along the direction to the hole. The method can be applied for hole-free in many cases as edited model, the model scanned from 3d scanning device and so on. After our up-sampling, the model holds the same geometry as the original model.