⚡Status Update: [2021/06/08] This paper has been accepted by the IEEE Transactions on Visualization and Computer Graphics (TVCG).

by Zheng Liu, Yanlei Li, Weina Wang, Ligang Liu, and Renjie Chen(Corresponding author)

MeshTGV is an efficient numerical framework to discretize TGV over triangular meshes. Based on this discretization, a vectorial TGV regularization model is proposed to restore the face normal field. Then, we introduce an efficient and effective algorithm to solve the optimization problem.

MeshTGV
 │
 │── data                  [//comparison data used in our paper]
 │     │—— CAD
 │     │── Kinect
 │     │── NonCAD
 │
 │—— MeshTGV-1.2.0.exe     [//executable file to run our algorithm]
 │—— README.md

1. double click the executable file.

2. load a mesh file (.obj) :

   Menu → Model → Load Mesh → Choose your noisy mesh

3. open the MeshTGV algorithm panel :

   Menu → Denoising → MeshTGV

4. set parameters then click the Run button.

5. get output :

   • Visualization output : showed in the mainwindow.
   • Norm result output : automaticlly saved in the same folder with the input Noisy Mesh.
   • Mesh result output : You need to save the Mesh result manually : Menu → Model → Save

Please see Section 6.1 Parameters Setting in our paper for more details of parameters.

There are four files in each model's folder. For example, in Block's folder :

 data                  [//comparison data used in our paper]
   │—— CAD
   │    │—— Block
   │    │    │—— groundtruth.obj
   │    │    │—— noisy.obj
   │    │    │—— tgv_filtered_normals-aad=xxx.txt
   │    │    │—— tgv_result_mesh.obj
   │    │—— ...
   │
   │── Kinect
   │── NonCAD

• groundtruth.obj : The GroundTruth data of model.

• noisy.obj : The Noise data of model.

• tgv_filtered_normals-aad=xxx.txt : The result normals saved after MeshTGV Normal Filtering but before Vertex Updating. "aad=xxx" in the filename means the Average Angle Deviation(θ) of the result normals is "xxx".

• tgv_result_mesh.obj : The result mesh saved after MeshTGV Normal Filtering and Vertex Updating.

If you find this work helpful please consider citing it :

@article{Liu2021MeshTGV,
  title={Mesh Total Generalized Variation for Denoising},
  author={Liu, Zheng and Li, Yanlei and Wang, Weina and Liu, Ligang and Chen, Renjie},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2021},
  doi={10.1109/TVCG.2021.3088118},
}