Important Note:

This repository alone is not a complete code. Please also check the repository for segmentation part [here].

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]. The low-dimension projection to 3-dimension and its filtering code are available in the repository.

Please refer to the [project page] 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, 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

Install dependencies

pip3 install -r requirements.txt

Clone or download this repository.
Download the pre-trained model.
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")
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 Likewise, our code (the tools in "kaffe" directory) is benefited from Caffe to 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}
    }

zhuo2015/SIGGRAPH18SSS