This project is based on the workshop paper "Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface". First part of this project deals with parameterization and second deals with learning of shapes from geometry images.
If you find this project useful in your work, please consider citing:
@inproceedings{jain2019learning,
title={Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface},
author={Jain, Hardik and Wöllhaf, Manuel and Hellwich, Olaf},
booktitle={The IEEE International Conference on Computer Vision (ICCV) Workshops},
year={2019}
}
Code for Parameterization has been written in C++ and requires:
- CGAL Fork with Iterative Parameterization Implementation
- Boost
- OpenCV
Deep network code is based on Tensorflow and is tested on Ubuntu with:
- python (3.5.2)
- tensorflow-gpu (1.14)
- scikit-image (0.15.0)
- numpy (1.16.5)
- natsort
- tqdm
Code contains functionality for:
- slicing the mesh (--slice)
- Iterative Surface Parameterization with n iterations (--sPI n)
- Compute Geometry Image (of size im) from the parameterized representation (--m2G im)
- Remesh point cloud from Geometry Image (--G2o)
python based functionality which contains:
- generating curvature mask from normalGI
- tensorflow model
- docker image
- python scripts to train and test the model
- trained airplane and car models (https://www.dropbox.com/sh/3lkfj03c1kmbs8u/AAAnbvxsarmWJ9fkiB4CtREra?dl=0)