A Novel Structure Adaptive Algorithm for Feature-preserving 3D Mesh Denoising

In this paper, we propose a novel algorithm for 3D mesh filtering (Structural Adaptive Filtering, SAF) based on mesh structural adaptation. As we all know, 3D meshes mainly have three types of geometric features: corners, edges, and planes. Therefore, we designed a protection mechanism for these three types of features to achieve the feature-preserving denoising. In the first step, for the faces normals to be processed, we build a variable set of similarity between the face normal and the neighborhood faces normal, calculate their coefficient of variation, variance, and quartile difference, and then select the neighborhood face normals with high similarity to update the current normals through self adaptation of these variables. In the second step, all vertices complete the iterative update of vertex coordinates according to the filtered face normals. Unlike existing 3D mesh denoising algorithms, which have too many parameters to manually set thresholds and are sensitive to parameters, SAF is based on the geometry of local faces (no need to manually set denoising thresholds). SAF only needs to set the iterative parameters to complete high-performance feature-preserving filtering, which has high practical value. We demonstrate through extensive experimental data that SAF outperforms or is comparable to state-of-the-art methods in feature-preserving denoising at different noise levels.

I. Introduction

Signals have more or less noise in the process of generation or transmission. Therefore, filtering algorithm has always been the most basic and important data preprocessing tool, and it has been an enduring research topic. Filtering algorithms can be roughly divided into two categories, local-based filtering and global-based filtering. In general, the filtering effect of the global-based filtering algorithm is better than that of the local-based filtering algorithm. However, compared with local-based filtering algorithms, its computational complexity is high. Moreover, the real-time performance of the algorithm has always been an important indicator to measure the pros and cons of the algorithm, and the performance of the current local-based filtering algorithm is getting closer and closer to the global-based filtering algorithm. Therefore, local-based filtering algorithms are increasingly favored by researchers and users.