Implicit Feature Networks for Texture Completion from Partial 3D Data
Julian Chibane, Gerard Pons-Moll
Paper - Project Website - Arxiv - Published in European Conference on Computer Vision (ECCV) Workshops.
If you find our code or paper usful for your project, please consider citing:
@inproceedings{chibane20ifnet,
title = {Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion},
author = {Chibane, Julian and Alldieck, Thiemo and Pons-Moll, Gerard},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {jun},
organization = {{IEEE}},
year = {2020},
}
and
@inproceedings{chibane2020ifnet_texture,
title = {Implicit Feature Networks for Texture Completion from Partial 3D Data},
author = {Chibane, Julian and Pons-Moll, Gerard},
booktitle = {European Conference on Computer Vision (ECCV) Workshops},
month = {August},
organization = {{Springer}},
year = {2020},
}
A linux system with cuda 9.0 or above is required for the project.
Please clone the repository and navigate into it in your terminal, its location is assumed for all subsequent commands.
The if-net_env.yml file contains necessary python dependencies for the project.
To conveniently install them automatically with anaconda you can use:
conda env create -f if-net_env.yml
conda activate tex_if-net
Install the library of the workshop with :
cd data_processing/eccv2020-sharp-workshop
pip install -r requirements.txt
pip install .
cd ../..
Please download and unzip the data of Challange 1 Track 1 into dataset/SHARP2020/ such that the data is stored as dataset/SHARP2020/challenge1-track1/{test,train}/File_ID/Files.
For each human scan we create 4 different, random incomplete ones with
python -m sharp shoot_dir dataset/SHARP2020/challenge1-track1 dataset/SHARP2020/challenge1-track1 --n-shapes 4
and convert everything to .obj-Format
python data_processing/convert_to_obj.py.
We sample points on the full colored surface, extract the corresponding RGB color and save it as training data with
python data_processing/sample_RGB_GT.py config/SHARP2020/track1.yaml.
Next, we create the input for the IF-Net (a colored, incomplete shape and a complete but uncolored shape) with
python data_processing/sample_voxelized_colored_pointcloud.py config/SHARP2020/track1.yaml.
A data split into training, test and validation is created with
python data_processing/create_split.py config/SHARP2020/track1.yaml.
A pretrained model can be found here.
A model is trained to predict the correct color of a surface point given colored, partial human and ground truth (complete), uncolored human. Use
python train.py config/SHARP2020/track1.yaml
to train until convergence. Generation on held out test data is done with
python generate.py config/SHARP2020/track1.yaml
automatically using the best model (lowest validation error).
You can also alter the generation code to change the ground truth (complete), uncolored human input to a surface reconstruction. We used a standard IF-Net for surface reconstruction.
For questions and comments regarding the code please contact Julian Chibane via mail.
Copyright (c) 2020 Julian Chibane, Max-Planck-Gesellschaft
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