An efficient approach for feature-preserving mesh denoising

With the growing availability of various optical and laser scanners, it is easy to capture different kinds of mesh models which are inevitably corrupted with noise. Although many mesh denoising methods proposed in recent years can produce encouraging results, most of them still suffer from their computational efficiencies. In this paper, we propose a highly efficient approach for mesh denoising while preserving geometric features. Specifically, our method consists of three steps: initial vertex filtering, normal estimation, and vertex update. At the initial vertex filtering step, we introduce a fast iterative vertex filter to substantially reduce noise interference. With the initially filtered mesh from the above step, we then estimate face and vertex normals: an unstandardized bilateral filter to efficiently smooth face normals, and an efficient scheme to estimate vertex normals with the filtered face normals. Finally, at the vertex update step, by utilizing both the filtered face normals and estimated vertex normals obtained from the previous step, we propose a novel iterative vertex update algorithm to efficiently update vertex positions. The qualitative and quantitative comparisons show that our method can outperform the selected state of the art methods, in particular, its computational efficiency (up to about 32 times faster).