Based on noise intensity, in this paper, we propose a feature-preserving smoothing algorithm for point-sampled geometry (PSG). The noise intensity of each sample point on PSG is first measured by using a combined criterion. Based on mean shift clustering, the PSG is then clustered in terms of the local geometry-features similarity. According to the cluster to which a sample point belongs, a moving least squares surface is constructed, and in combination with noise intensity, the PSG. is finally denoised. Some experimental results demonstrate that the algorithm is robust, and can smooth out the noise efficiently while preserving the surface features. 1
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