Skip to content
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
C++ C Python Cuda CMake Makefile Shell
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
3rd first commit Mar 30, 2019
data Add code generator to preprocess Jun 29, 2019
evaluate
img first commit Mar 30, 2019
src
.gitignore
LICENSE
README.md Update README.md Jul 12, 2019

README.md

TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes

Source code for the paper:

Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Niessner, and Leonidas Guibas. TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes, CVPR 2019 ([Oral Presentation]).

TextureNet Teaser

Usage Pipeline

Data Preparation

Please refer to data directory for details.

Training, Testing and Result Generation

Please refer to src directory for details.

Preparing the Final Results and Evaluation Scores

Please refer to evaluate directory for details.

Author

© 2019 Jingwei Huang All Rights Reserved

IMPORTANT: If you use this code please cite the following in any resulting publication:

@inproceedings{huang2019texturenet,
  title={Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes},
  author={Huang, Jingwei and Zhang, Haotian and Yi, Li and Funkhouser, Thomas and Nie{\ss}ner, Matthias and Guibas, Leonidas J},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4440--4449},
  year={2019}
}
You can’t perform that action at this time.