SSLayout360

We provide the data splits for PanoContext, Stanford 2D-3D, and MatterportLayout used in our CVPR 2021 paper for semi-supervised indoor layout estimation. At this time, we are unable to release the training code and pretrained models.

   

Data Download

To gain access to the actual image and annotation files, please use the following instructions:

• Download pre-processed PanoContext and Stanford 2D-3D datasets in .pkl format from LayoutNetv2.
• Download the Matterport3D dataset.
• Follow the Matlab code in PanoBasic/demo_matterport.m to stitch the Matterport3D skybox images into equirectangular panoramas.
• Run the script provided in PanoAnnotator/pre-process to generate Manhattan-aligned panoramas.

Usage

The data splits are provided in data. Each row in the .csv file corresponds to a file ID and its membership in the respective "labeled subset" column, which indicates the amount of labeled instances sampled from the training set. We report performance metrics in Tables 1 and 2 of our paper based on these splits.

To use MatterportLayout, after you have downloaded the Matterport3D dataset and stitched the skybox images into equirectangular panoramas, use the following instructions:

• Download and extract the layout annotations to the data/matterportlayout directory.

  $ curl -LJO https://github.com/ericsujw/Matterport3DLayoutAnnotation/raw/master/label_data.zip
  $ unzip label_data.zip -d ./data/matterportlayout/

• Download the provided train/val/test partitions to the data/matterportlayout directory.

• Run the following commands to convert the layout annotations in .json to .txt format as proposed by HorizonNet. Here, we assume --img-root /path/to/skybox/panos is a flat directory, with all filenames under one parent directory.

  # Make `train` split
  python utils/make_matterportlayout.py \
      --img-root /path/to/skybox/panos/ \
      --ann-root data/matterportlayout/label_data/ \
      --dataset data/matterportlayout/mp3d_train.txt \
      --split-name train --out-dir ./mp3d_layout
  
  # Make `val` split
  python utils/make_matterportlayout.py \
      --img-root /path/to/skybox/panos/ \
      --ann-root data/matterportlayout/label_data/ \
      --dataset data/matterportlayout/mp3d_val.txt \
      --split-name val --out-dir ./mp3d_layout
      
  # Make `test` split
  python utils/make_matterportlayout.py \
      --img-root /path/to/skybox/panos/ \
      --ann-root data/matterportlayout/label_data/ \
      --dataset data/matterportlayout/mp3d_test.txt \
      --split-name test --out-dir ./mp3d_layout

• You should have an output mp3d_layout directory with the following structure.

  mp3d_layout/
  |--train/
  |  |--img/
  |  |  |--*.png
  |  |--label_cor/
  |  |  |--*.txt
  |--val/
  |  |--img/
  |  |  |--*.png
  |  |--label_cor/
  |  |  |--*.txt
  |--test/
  |  |--img/
  |  |  |--*.png
  |  |--label_cor/
  |  |  |--*.txt
  

License

This repository is released under the Apache-2.0 License. All datasets derived from Matterport3D are released under the Matterport3D Terms of Use. The user is instructed to download the datasets from their respective sources and agrees to their Terms of Use.

Citation

@inproceedings{TranSSLayout360,
  author={Phi Vu Tran},
  title="{SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama}",
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}