indoor-scene-layout-labeling

LSUN dataset indoor scene layout labeling via transfer learning with segmentation ResNet101

Prerequisites

• cv2
• PyTorch
• Torchvision

Usage

The root directory should be namded "Bounding_box/"

• To label image using trained model:

$python main.py "filename"

• To train a new model:

$python learn.py "filename"

alternatively, utilize cloud GPUs with "BBox.ipynb". This requires uploading LSUN data as well.

Trained models should be saved in the root directory

Modify training paramters in "params.py"

Data

Download LSUN room dataset at LSUN and place under "data/" directory

Credit: liamw96

Example

Sample labeling:

Scenes are assigned four labels: walls (includinig ceiling and floor), ceiling lines, floor lines and wall lines

License

see the LICENSE.md file for details

References

This project is generally implemented based on:

1. Mallya, A. & Lazebnik, S. (2015). Learning Informative Edge Maps for Indoor Scene Layout Prediction
2. Schwing, A. G. & Urtasun, R. (2012). Efficient Exact Inference for 3D Indoor Scene Understanding
3. Hedau, V., Hoiem, D. & Forsyth, D. A. (2009). Recovering the spatial layout of cluttered rooms
4. Rother, C. (2000). A New Approach for Vanishing Point Detection in Architectural Environments
5. Tardif, J.-P. (2009). Non-iterative approach for fast and accurate vanishing point detection
6. Denis, P., Elder, J. H. & Estrada, F. J. (2008). Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery

Contact me for more detailed report on the implementation.

Contact

huaiyuc@seas.upenn.edu