https://drive.google.com/open?id=1rmOggF0ivB42KozMX3sQGD1CkZNOGCmM
https://drive.google.com/open?id=1zLQd68O73ZiwZ8S8qsLwwGYDcC5PiEdG
https://drive.google.com/open?id=1wTE721q0r66Z6yyN68O1Tz4Bg5-aYnq3
python 2.7.6
tensorflow 1.2.0
numpy 1.13.3
scipy 0.19.0
matplotlib 2.0.2
skimage 0.13.0
source venv/bin/activate
python main_3D-RecGAN++.py
python demo_3D-RecGAN++.py
source venv/bin/activate
python main.py
@inProceedings{Yang18,
title={Dense 3D Object Reconstruction from a Single Depth View},
author = {Bo Yang
and Stefano Rosa
and Andrew Markham
and Niki Trigoni
and Hongkai Wen},
booktitle={TPAMI},
year={2018}
}
System Requirements:
Google Cloud Platform - Deep Learning VM Input Framework: PyTorch 1.4 + fast.ai 1.0 (CUDA 10.0) GPU: 1, nvidia-tesla-k80
We have referred to the code provided by the following citation for our implementation:
@inproceedings{Occupancy Networks,
title = {Occupancy Networks: Learning 3D Reconstruction in Function Space},
author = {Mescheder, Lars and Oechsle, Michael and Niemeyer, Michael and Nowozin, Sebastian and Geiger, Andreas},
booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}
Our modifications to the original architecture is documented within the code.
conda env create -f environment.yaml conda activate mesh_funcspace
python setup.py build_ext --inplace
bash scripts/download_data.sh
python train.py <CONFIG_FILE_DIRECTORY/onet.yaml>
python generate.py <CONFIG_FILE_DIRECTORY/onet_pretrained.yaml