NormalNet: Learning-Based Mesh Normal Denoising via Local Partition Normalization

Abstract

Mesh denoising is a critical technology in geometry processing that aims to recover high-fidelity 3D mesh models of objects from noise-corrupted versions. In this work, we propose a learning-based mesh normal denoising scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NormalNet</i> , which employs deep networks to find the correlation between the volumetric representation and denoised face normal. Overall, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NormalNet</i> follows the iterative framework of filtering-based mesh denoising. During each iteration, firstly, a local partition normalization strategy is applied to split the local structure around each face into dense voxels, in which both the structure and face normal information can be preserved during this transformation. Benefiting from the thorough information preservation, we can use simple residual networks, which employ the volumetric representation as the input and produce the learned denoised face normal, to achieve satisfactory results. Finally, the vertex positions are updated according to the denoised normals. Besides introducing normalization into mesh denoising, our main contributions include a classification-based training faces selection strategy for balancing the training set and a mismatched-faces rejection strategy for removing the mismatched faces between noisy mesh and ground truth. Compared to state-of-the-art works, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NormalNet</i> can effectively remove noise while preserving the original features and avoiding pseudo-features.