Indoor Scene Layout Estimation from a Single Image

Paper

Indoor Scene Layout Estimation from a Single Image.

Hung Jin Lin, Sheng-Wei Huang, Shang-Hong Lai, Chen-Kuo Chiang National Tsing Hua University.
International Conference on Pattern Recognition (ICPR), 2018.
Read in public [PDF].

Citation

If you find the work is useful, please use the following BibTeX entry.

@inproceedings{lin2018layoutestimation,
    Author = {Hung Jin Lin and Sheng-Wei Huang and Shang-Hong Lai and Chen-Kuo Chiang},
    Title = {Indoor Scene Layout Estimation from a Single Image},
    Booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)},
    Year = {2018}
}

Prerequisite

• Python 3.6+
• PyTorch 1.0+
• OneGAN newest is okay.
• pip install -e requirements.txt

Dataset

• LSUN Layout Challenge
  • Unfortunately, the website of LSUN Layout Challenge is down.
• However, we can find them from below links.
  • Full images and labels dataset: LSUN2016_surface_relabel.zip from @yuzhuoren.

    Recommend to use this, the original ground truth of LSUN Room challenge are not perfect.

  • Data splits for training.mat, validation.mat and testing.mat from the web archive.

Checkpoint

Pre-trained weight at Google Drive.

Cannot provide original checkpoint in paper due to the agreement, so this is a re-trained checkpoint for fast evaluation.

Updated Jan 2021.

Usage

Thanks @shuuchen for an all-in-one project, you may also refer to https://github.com/shuuchen/lsun-room-dsc!

• Training

  • Dataset LSUN Room Layout Dataset into the folders of ./data/lsun_room.

    • images/: RGB image *.jpg of indoor room scene
    • layout_seg/: layout ground truth *.mat planar segmentation

  • Trained model will be saved to folder ./ckpts

  python main.py --phase train --edge_factor 0.2 --l2_factor 0.2 --name baseline

• Validation

  • Validate on LSUN-Room / Hedau datasets.

  python main.py --phase eval --dataset hedau --folder ./data/hedau --pretrain_path {checkpoint_path}

• Testing

  • On your photo or image sequence

  # single image
  python demo.py image --weight {checkpoint_path} --path {image_filepath}
  
  # images in folder
  python demo.py image --weight {checkpoint_path} --path {image_folder}

  • On video or webcam

  # video
  python demo.py video --weight {checkpoint_path} --path {test_video}
  
  # webcam
  python demo.py video --weight {checkpoint_path} --device 0

• Toolkit

  • Official LSUN Room Layout Dataset toolkit in folder ./lib/lsun_toolkit

  # this is my modified script for usage demonstration,
  # you may need to modify the official Matlab code to evaluate your results.
  matlab -nojvm -nodisplay -nosplash -r "demo('$EXPERIMENT_OUTPUT_FOLDER'); exit;"

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