8 research outputs found
Robust feature-preserving mesh denoising based on consistent subneighborhoods
In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved. © 2010 IEEE.link_to_subscribed_fulltex
Robust Feature-Preserving Mesh Denoising Based on Consistent Sub-Neighborhoods
published_or_final_versio
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