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TOM-Net

TOM-Net: Learning Transparent Object Matting from a Single Image, CVPR 2018 (Spotlight), <br> Guanying Chen*, Kai Han*, Kwan-Yee K. Wong <br> (* equal contribution)

This paper addresses the problem of transparent object matting from a single image. <br>

Dependencies

TOM-Net is implemented in Torch and tested with Ubuntu 14.04. Please install Torch first following the official document.

• python 2.7
• numpy
• cv2
• CUDA-8.0
• CUDNN v5.1
• Torch STN (qassemoquab/stnbhwd)

  # Basic installation steps for stn
  git clone https://github.com/qassemoquab/stnbhwd.git
  cd stnbhwd
  luarocks make
  

Overview

We provide:

• Pretrained model
• Datasets: Train (40GB), Validation (196MB), Test (179MB)
• Code to test model on new images
• Evaluation code on both the validation and testing data
• Instructions to train the model
• Example code for synthetic data rendering
• Code and models used in the journal extension <b>(New!)</b>

If the automatic downloading scripts are not working, please download the trained models and the introduced dataset from BaiduYun (Models and Datasets).

Testing

Download Pretrained Model

sh scripts/download_pretrained_model.sh

If the above command is not working, please manually download the trained models from BaiduYun (Models, Datasets) and put them in ./data/models/.

Test on New Images

# Replace ${gpu} with the selected GPU ID (starting from 0)

# Test a single image without having the background image
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_model.lua -input_img images/bull.jpg 

# You can find the results in data/TOM-Net_model/

Evaluation on Synthetic Validation Data

# Download synthetic validation dataset
sh scripts/download_validation_dataset.sh

# Quantitatively evaluate TOM-Net on different categories of synthetic object 
# Replace ${class} with one of the four object categories (glass, water, lens, cplx)
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_synth_data.lua -img_list ${class}.txt

# Similarly, you can find the results in data/TOM-Net_model/

Evaluation on Real Testing Data

# Download real testing dataset, 
sh scripts/download_testing_dataset.sh

# Test on sample images used in the paper
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_model.lua -img_list Sample_paper.txt

# Quantitatively evaluate TOM-Net on different categories of real-world object 
# Replace ${class} with one of the four object categories (Glass, Water, Lens, Cplx)
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_model.lua -img_list ${class}.txt  

Training

To train a new TOM-Net model, please follow the following steps:

• Download the training data

# The size of the zipped training dataset is 40 GB and you need about 207 GB to unzip it.
sh scripts/download_training_dataset.sh

• Train CoarseNet on simple objects

CUDA_VISIBLE_DEVICES=$gpu th main.lua -train_list train_simple_98k.txt -nEpochs 13 -prefix 'simple'
# Please refer to opt.lua for more information about the training options

# You can find log file, checkpoints and visualization results in data/training/simple_*

• Train CoarseNet on both simple and complex objects

# Finetune CoarseNet with all of the data
CUDA_VISIBLE_DEVICES=$gpu th main.lua -train_list train_all_178k.txt -nEpochs 7 -prefix 'all' -retrain data/training/simple_*/checkpointdir/checkpoint13.t7

# You can find log file, checkpoints and visualization results in data/training/all_*

• Train RefineNet on both simple and complex objects

CUDA_VISIBLE_DEVICES=$gpu th refine/main_refine.lua -train_list train_all_178k.txt -nEpochs 20 -coarse_net data/training/all_*/checkpointdir/checkpoint7.t7 
# Train RefineNet with all of the data
# Please refer to refine/opt_refine.lua for more information about the training options

# You can find log file, checkpoints and visualization results in data/training/all_*/refinement/

Synthetic Data Rendering

Please refer to TOM-Net_Rendering for sample rendering codes.

Codes and Models Used in the Journal Extension (IJCV)

Test TOM-Net^+Bg and TOM-Net^+Trimap on Sample Images

# Download pretrained models
sh scripts/download_pretrained_models_IJCV.sh

# Test TOM-Net+Bg on sample images
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_model.lua -input_root images/TOM-Net_with_Trimap_Bg_Samples/ -img_list img_bg_trimap_list.txt -in_bg -c_net data/TOM-Net_plus_Bg_Model/CoarseNet_plus_Bg.t7 -r_net data/TOM-Net_plus_Bg_Model/RefineNet_plus_Bg.t7 
# You can find the results in data/TOM-Net_plus_Bg_Model/*

# Test TOM-Net+Trimap on sample images
CUDA_VISIBLE_DEVICES=${gpu} th eval/run_model.lua -input_root images/TOM-Net_with_Trimap_Bg_Samples/ -img_list img_bg_trimap_list.txt -in_trimap -c_net data/TOM-Net_plus_Trimap_Model/CoarseNet_plus_Trimap.t7 -r_net data/TOM-Net_plus_Trimap_Model/RefineNet_plus_Trimap.t7 
# You can find the results in data/TOM-Net_plus_Trimap_Model/*

Train TOM-Net^+Bg and TOM-Net^+Trimap

To train a new TOM-Net^+Bg or TOM-Net^+Trimap model, please follow the same procedures as training TOM-Net, except that you need to append -in_bg or -in_trimap at the end of the commands.

Citation

If you find this code or the provided data useful in your research, please consider cite the following relevant paper(s):

@inproceedings{chen2018tomnet,
  title={TOM-Net: Learning Transparent Object Matting from a Single Image},
  author={Chen, Guanying and Han, Kai and Wong, Kwan-Yee~K.},
  booktitle={CVPR},
  year={2018}
}

@inproceedings{chen2019LTOM,
  title={Learning Transparent Object Matting},
  author={Chen, Guanying and Han, Kai and Wong, Kwan-Yee~K.},
  booktitle={IJCV},
  year={2019}
}