Pattern Recognition Letters
A cascaded approach for feature-preserving surface mesh denoising
Computer-Aided Design
Surface mesh denoising with normal tensor framework
Graphical Models
Empirical mode decomposition on surfaces
Graphical Models
A framework for 3D model reconstruction in reverse engineering
Computers and Industrial Engineering
Mesh denoising via L0 minimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Mesh saliency with global rarity
Graphical Models
CAD/Graphics 2013: Feature-preserving filtering with L0 gradient minimization
Computers and Graphics
Robust reconstruction of 2D curves from scattered noisy point data
Computer-Aided Design
Consolidation of low-quality point clouds from outdoor scenes
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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Decoupling local geometric features from the spatial location of a mesh is crucial for feature-preserving mesh denoising. This paper focuses on first order features, i.e., facet normals, and presents a simple yet effective anisotropic mesh denoising framework via normal field denoising. Unlike previous denoising methods based on normal filtering, which process normals defined on the Gauss sphere, our method considers normals as a surface signal defined over the original mesh. This allows the design of a novel bilateral normal filter that depends on both spatial distance and signal distance. Our bilateral filter is a more natural extension of the elegant bilateral filter for image denoising than those used in previous bilateral mesh denoising methods. Besides applying this bilateral normal filter in a local, iterative scheme, as common in most of previous works, we present for the first time a global, noniterative scheme for an isotropic denoising. We show that the former scheme is faster and more effective for denoising extremely noisy meshes while the latter scheme is more robust to irregular surface sampling. We demonstrate that both our feature-preserving schemes generally produce visually and numerically better denoising results than previous methods, especially at challenging regions with sharp features or irregular sampling.