Bi-Normal Mesh Smoothing Based on Vertex Feature

Wuli Wang,Liming Duan

doi:10.1109/access.2020.3022628

Abstract

Aiming at the issue that mesh smoothing is hard to balance in terms of noise removing and feature preserving, in this article, we combine the facet normal representing global geometric features of the mesh with the vertex normal characterizing local details of the mesh and propose a bi-normal mesh smoothing method based on vertex feature selection (BNBVF). Firstly, the guided filtering algorithm is extended to calculate accurately the facet normal in the field of geometric processing. The key of this portion is computing the guided normal of facet by using a geometric neighboring patch with the most consistent normal. Then, an adaptive tensor voting method is employed to divide the vertices of the mesh into feature vertices and non-feature vertices. Thirdly, a method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate the normal of feature vertex, and the weighting average of first-order neighborhood facets of the vertex is applied to compute the normal of non-feature vertex. Finally, the vertices of the mesh are updated iteratively by combining the geometric information of the facet normal and vertex normal to achieve mesh smoothing. Experimental results demonstrate that the superior performance of our proposed algorithm to state-of-the-art approaches in feature preserving and error reducing.

Highlights

Industrial computed tomography (CT) can clearly, accurately and intuitively display the internal structure, composition, material and defect condition of the detected object in the form of two-dimensional sectional image or threedimensional image without damage to the detected object
The existing mesh smoothing methods have achieved satisfactory results, there remains still a challenging issue that non-uniform sampling and multi-scale triangular mesh reconstructed by industrial CT data can effectively preserve geometric detail of the mesh while smoothing
EXPERIMENTAL RESULTS AND ANALYSIS To validate the effectiveness in both noise eliminating and feature retaining of the proposed method, the method is compared against the classical and state-of-the-art methods on some meshes, such as, Laplacian (LAP) [3], bilateral filtering (BF) [8], bilateral normal filtering (BNF) [10], joint bilateral filtering (JBF) [14], bi-normal filtering based on optimization (BNFBO) [12], and PcFilter [34]

Summary

INTRODUCTION

Industrial computed tomography (CT) can clearly, accurately and intuitively display the internal structure, composition, material and defect condition of the detected object in the form of two-dimensional sectional image or threedimensional image without damage to the detected object. Fleishman et al [8] extended firstly the bilateral filtering algorithm that is well filtering effect in the field of image processing, and applied it to smooth the mesh by means of a fast one-step iterative scheme. This method cannot always accurately preserve the details of the mesh. A two-stage method (or its variants) [12]–[17] that the facet normal is firstly calculated and the vertices are iteratively updated according to the facet normal is proposed to achieve mesh smoothing, and it can better preserve the geometric and detailed features of the mesh with certain noise. The work foci of this article is three-fold: (1) A novel mesh smoothing method that considers both facet normal and vertex normal is developed. (2) The authors extend the guided filtering to calculate accurately the facet normal according to the patch whose consistency with the facet is best. (3) A method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate precisely the normal of feature vertex

COMPUTING THE FACET NORMAL FIELD BASED ON GUIDED FILTERING

FACET NORMAL FILTERING

CONSTRUCTING THE GUIDED NORMAL OF FACET

EXPERIMENTAL RESULTS AND ANALYSIS

CONCLUSION