Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/chaene/hsp
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/chaene/hsp
- Owner: chaene
- License: gpl-2.0
- Created: 2018-02-16T18:12:45.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-05-23T18:08:55.000Z (over 6 years ago)
- Last Synced: 2024-05-31T17:28:13.250Z (6 months ago)
- Language: Lua
- Size: 47.9 KB
- Stars: 30
- Watchers: 4
- Forks: 7
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Hierachical Surface Prediction
## Installation
Install [torch](http://torch.ch/)
Download [CImg](http://cimg.eu/) and place it in the torch-hsp subfolder. The file "CImg.h" needs to be in the path "torch-hsp/CImg/".
Install the torch package torch-hsp by running "luarocks make hsp-1.0-0.rockspec" in the torch-hsp folder
## Running Demo
A demo script is included which reconstructs a single image and outputs a mesh as obj file. It needs as input the pretrained network provided [here](https://drive.google.com/file/d/1it00XjWc7PnKAwVhPEtl2V96g3RPbi2V/view?usp=sharing).
th hspDemo.lua <GPU ID> <Trained Network File Name> <Input Image File Name>
## Training Network
Example parameter files are provided [here](https://drive.google.com/file/d/1it00XjWc7PnKAwVhPEtl2V96g3RPbi2V/view?usp=sharing).
The data is provided [here](https://drive.google.com/file/d/1xtJz5CEEPgYOtWP6Dr6nUWbUXPDMswh0/view?usp=sharing).
To train a network the paths to the shapenet dataset and the output folder in the "parameters.lua" file need to be adjusted first.
th trainNetworkHierarchical.lua <GPU ID> <Parameter File Name>
## License and Citation
The code is released as GPLv2.
When using the provided data make sure to respect the shapenet [license](https://shapenet.org/terms).
Please cite our paper when using the code.
C. Häne, S. Tulsiani, J. Malik, Hierarchical Surface Prediction for 3D Object Reconstruction, Proc. Int. Conf. on 3D Vision (3DV), 2017