MeshDenoising
Description
Denoise your mesh Mesh models generated by 3D scanner always contain
noise. It is necessary to remove the noise from the meshes. Mesh
denoising: remove noises, feature-preserving
https://www.cs.cf.ac.uk/meshfiltering/index_files/Doc/Random%20Walks%20for%20Mesh%20Denoising.ppt
settings
Name |
Description |
|---|---|
input |
Input Mesh (OBJ file format) |
Denoising Iterations |
Number of denoising iterations (0 - 30) |
Mesh Update Closeness Weight |
Closeness weight for mesh update, must be positive(0 - 0.1) (0.001) |
Lambda |
|
Eta |
Gaussian standard deviation for spatial weight, scaled by the average distance between adjacent face centroids. Must be positive.(0.0 - 20) (1.5) |
Gaussian standard deviation for guidance weight (0.0-10) (1.5) |
|
Gaussian standard deviation for signal weight. (0.0-5) (0.3) |
|
Mesh Update Mesh |
Mesh Update Method * ITERATIVEUPDATE (default): ShapeUp styled iterative solver * POISSONUPDATE: Poisson-based update from [Wang et al. 2015] (0, 1) |
Verbose Level |
[‘fatal’, ‘error’, ‘warning’, ‘info’, ‘debug’, ‘trace’] |
Output |
|
Mesh Update Method
https://www.researchgate.net/publication/275104101_Poisson-driven_seamless_completion_of_triangular_meshes
Wang et al.
https://dl.acm.org/citation.cfm?id=2818068
Detailed Description
A larger value of Lambda or Eta leads to a smoother filtering result.
From: “Static/Dynamic Filtering for Mesh Geometry” by Zhang Et al. https://arxiv.org/pdf/1712.03574.pdf