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https://github.com/iyah4888/SIGGRAPH18SSS
SIGGRAPH2018, Semantic Soft Segmentation, http://people.inf.ethz.ch/aksoyy/sss/
https://github.com/iyah4888/SIGGRAPH18SSS
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SIGGRAPH2018, Semantic Soft Segmentation, http://people.inf.ethz.ch/aksoyy/sss/
- Host: GitHub
- URL: https://github.com/iyah4888/SIGGRAPH18SSS
- Owner: iyah4888
- License: mit
- Created: 2018-07-29T20:41:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-07T22:54:34.000Z (about 4 years ago)
- Last Synced: 2024-05-19T23:35:51.801Z (6 months ago)
- Language: Python
- Size: 1.12 MB
- Stars: 456
- Watchers: 24
- Forks: 107
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Important Note:
**This repository alone is not a complete code. Please also check the repository for segmentation part [[here](https://github.com/yaksoy/SemanticSoftSegmentation)].**
**Also, please readme README carefully.**
# Semantic Soft Segmentation, ACM SIGGRAPH 2018
This repository includes the semantic feature (128-D) generation approach presented in
Yagiz Aksoy, Tae-Hyun Oh, Sylvain Paris, Marc Pollefeys and Wojciech Matusik, "Semantic Soft Segmentation", ACM Transactions on Graphics (Proc. SIGGRAPH), 2018
Also, note that this repository is NOT **stand-alone**.
The spectral segmentation implementation can be found [[here](https://github.com/yaksoy/SemanticSoftSegmentation)].
The low-dimension projection to 3-dimension and its filtering code are available in the repository.
Please refer to the [[project page](http://people.inf.ethz.ch/aksoyy/sss/)] for more information.
Note that only the feature generator is presented in this repository and the training code is not included.
# Requirements
Python 3.6, 1.8 >= TensorFlow >= 1.4 and other common packages listed in requirements.txt.
The code has been tested on {Linux Ubuntu 16.04, TensorFlow-GPU 1.4} and {Windows 10, TensorFlow-GPU 1.8} with Titan Xp.
# Installation
1. Install dependencies
```
pip3 install -r requirements.txt
```
2. Clone or download this repository.
3. Download the [pre-trained](http://cvg.ethz.ch/research/semantic-soft-segmentation/SSS_model.zip) model.
4. Extract the model and put the extracted "model" folder into the folder where the repository is cloned.
- e.g., If the repository is cloned at "/project/sss", then move the model to be "/project/sss/model")
5. Run "run_extract_feat.sh", which will process sample images in the "samples" folder. If you want to run your own images, notice that image files should be the PNG formats.
# Notes
Currently, the code only supports the PNG file format.
# Citation
If you use this code, please cite our paper:
```
@ARTICLE{sss,
author={Ya\u{g}{\i}z Aksoy and Tae-Hyun Oh and Sylvain Paris and Marc Pollefeys and Wojciech Matusik},
title={Semantic Soft Segmentation},
journal={ACM Transactions on Graphics (Proc. SIGGRAPH)},
year={2018},
pages = {72:1-72:13},
volume = {37},
number = {4}
}
```
This code is for protyping research ideas; thus, please use this code only for non-commercial purpose only.
# Credits
The part of the base codes (the tools in the "deeplab_resnet" directory) are borrowed from [(Re-)implementation of DeepLab-ResNet-TensorFlow](https://github.com/DrSleep/tensorflow-deeplab-resnet#using-your-dataset)
Likewise, our code (the tools in "kaffe" directory) is benefited from [Caffe to TensorFlow](https://github.com/ethereon/caffe-tensorflow)
Also, our architecture is implemented on top of the base architecture, DeepLab-ResNet-101.
```
@article{CP2016Deeplab,
title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
journal={arXiv:1606.00915},
year={2016}
}
```