7 research outputs found
Segmentation Based Mesh Denoising
Feature-preserving mesh denoising has received noticeable attention recently.
Many methods often design great weighting for anisotropic surfaces and small
weighting for isotropic surfaces, to preserve sharp features. However, they
often disregard the fact that small weights still pose negative impacts to the
denoising outcomes. Furthermore, it may increase the difficulty in parameter
tuning, especially for users without any background knowledge. In this paper,
we propose a novel clustering method for mesh denoising, which can avoid the
disturbance of anisotropic information and be easily embedded into
commonly-used mesh denoising frameworks. Extensive experiments have been
conducted to validate our method, and demonstrate that it can enhance the
denoising results of some existing methods remarkably both visually and
quantitatively. It also largely relaxes the parameter tuning procedure for
users, in terms of increasing stability for existing mesh denoising methods
NormalNet: Learning based Guided Normal Filtering for Mesh Denoising
Mesh denoising is a critical technology in geometry processing, which aims to
recover high-fidelity 3D mesh models of objects from noise-corrupted versions.
In this work, we propose a deep learning based face normal filtering scheme for
mesh denoising, called \textit{NormalNet}. Different from natural images, for
mesh, it is difficult to collect enough examples to build a robust end-to-end
training scheme for deep networks. To remedy this problem, we propose an
iterative framework to generate enough face-normal pairs, based on which a
convolutional neural networks (CNNs) based scheme is designed for guidance
normal learning. Moreover, to facilitate the 3D convolution operation in CNNs,
for each face in mesh, we propose a voxelization strategy to transform
irregular local mesh structure into regular 4D-array form. Finally, guided
normal filtering is performed to obtain filtered face normals, according to
which denoised positions of vertices are derived. Compared to the
state-of-the-art works, the proposed scheme can generate accurate guidance
normals and remove noise effectively while preserving original features and
avoiding pseudo-features