Abstract
In recent years, surface denoising has been a subject of intensive research in geometry processing. Most of the recent approaches for mesh denoising use a two-step scheme: normal filtering followed by a point updating step to match the corrected normals. In this paper we propose an adaptation of such two-step approaches for point-based surfaces, exploring three different weighting schemes for filtering normals. Moreover, we also investigate three techniques for normal estimation, analyzing the impact of each normal estimation method in the whole point-set smoothing process. Towards a quantitative analysis, in addition to conventional visual comparison, we evaluate the effectiveness of different choices of implementation using two measures, comparing our results against state-of-art point-based denoising techniques.