Feature-preserving mesh denoising via normal guided quadric error metrics

While modern optical and laser 3D scanners can generate high accuracy mesh models, to largely avoid their introducing noise which prohibits practical applications still results in high cost. Thus, optimizing noisy meshes while preserving their geometric details is necessary for production, which still remains as challenging work. In this paper we propose a novel and efficient two-stage feature-preserving mesh denoising framework which can remove noise while preserving fine features of a surface mesh. We improve the capability of feature preservation of our vertex updating scheme by employing an extension of the quadric error metrics (QEM), which can track and minimize updating errors and hence well preserve the overall shape as well as detailed features of a mesh. We further leverage vertex normals to guide the vertex updating process, as the normal field of a mesh reflects the geometry of the underlying surface. In addition, to obtain a more accurate normal field to guide vertex updating, we develop an improved normal filter by integrating advantages of existing filters. Compared with traditional gradient descent based schemes, our method performs better on challenging regions with rich geometric features. Moreover, a local entropy metric is proposed to measure stability of a mesh and the effectiveness of vertex updating algorithms. Qualitative and quantitative experiments demonstrate that our approach can effectively remove noise from noisy meshes while preserving or recovering geometrical features of original objects.