Mesh Classification and Denoising using the Cotangents Laplacian Operator [PDF]
The Mean, Gaussian and Principle components are based on "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds, Meyer et al"
Classification of the mesh is done by binning the gaussian and mean curvature using a normalized histogram. The normalized histogram acts as the PDF and the Wasserstein distance is used to compute the closest match.
| Clean | Noisy |
|---|---|
 |
 |
The weights for anisotropic smoothing for feature-preserving denoising of a noisy mesh.
To evalute the algorithm in MATLAB run
main.m
located in src/matlab
To evalute the algorithm in Python run
python main.py
located in src/python
If you use this software in your work, please cite it using the following metadata.
@software{Millerdurai_meshclassification_2022,
author = {Millerdurai, Christen and Usón Peirón, Javier and Schichtel, Marco},
month = {02},
title = {{Mesh Classification and Denoising using the Cotangents Laplacian Operator}},
url = {https://github.com/Chris10M/mesh-feature-detection},
version = {1.0.0},
year = {2022}
}