roomnet

This is a tensorflow implementation of room layout paper: RoomNet: End-to-End Room Layout Estimation.

Note: This is a simply out-of-interest experiemnt and I cannot guarantee to get the same effect of the origin paper.

Network

![] Here I implement two nets: vanilla encoder-decoder version and 3-iter RCNN refined version. As the author noted, the latter achieve better results.

Data

I use LSUN dataset and please download and prepare the RGB images and get a explorationo of the .mat file it includs because they contain layout type, key points and other information. Here I simply resize the image to (320, 320) with cubic interpolation and do the flip horizontally. (Note: When you flip the image, the order of layout key points should also be fliped.) You can see the preparation of data in prepare_data.py

Pre-requests:

You need to install tensorflow>=1.2, opencv, numpy, scipy and other basic dependencies.

How to use:

Training:

python main.py --train 0 or 1 --net vanilla ot rcnn --out_path path-to-output 

Testing:

python main.py --test 0 --net vanilla ot rcnn --out_path path-to-output 

(P.S. I may upload the pre-trained model later because currently I don't find a place to put it.)

Some Results: