aminshabani / extreme-indoor-sfm

Code and instructions for our paper: Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps, ICCV 2021.

Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps

Code and instructions for our paper: Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps, ICCV 2021.

Installation

First, clone our repo and install the requirements:

git clone https://github.com/aminshabani/extreme-indoor-sfm.git
cd extreme-indoor-sfm
pip install -r requirements.txt

The cose is based on pytorch and use Detectron2 for door/window detection, and HorizonNet for layout estimation.

Dataset

First, preprocess the panorama images of each house to be aligned with the Manhattan World. You can use the same script as previous methods on layout estimation. create a new dataset directory including a folder for each house and move the corrosponding panorama images to that folder. The directory structure should be as following:

extreme-indoor-sfm
├── dataset
│   ├── house1
│   │   ├──images
│   │   │   └── aligned_0.png
│   │   │   └── aligned_1.png
|   |   │   └── ...
|   |   └── floorplan.jpg
│   ├── house2
|   |   └── images
|   |   └── floorplan.jpg
|   └── ...
└── detection
└── ...

You can download some of the sample houses from this link. please see the panorama.py and house.py for more details.

Finally, add the names of the houses to test.txt. For the provided house for example, it should be:

0001
0002
...

Pre-trained Models

Please download the checkpoints from Google Drive and put them to the same directory as they are. You can also update the corresponding args in parser.py.

Floorplan estimation

Finally you can simple run the code by:

bash run.sh

The above command generate each module step-by-step and creates a new output directory in which you can find the predicted floorplans sorted by their score.

Meanwhile, you can also find the outputs of each module (detection, layout estimation, and room type predictions) in the dataset folder of each house.

extreme-indoor-sfm
├── dataset
│   ├── house1
|   |   └── images
|   |   └── detection_preds
|   |   └── ...
|   |   └── floorplan.jpg
└── ...
└── output

Citation

@InProceedings{Shabani_2021_ICCV,
author = {Shabani, Mohammad Amin and Song, Weilian and 
        Odamaki, Makoto and Fujiki, Hirochika and Furukawa, Yasutaka},
title = {Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
url = {https://aminshabani.github.io/publications/extreme_sfm/pdfs/iccv2021_2088.pdf}
}