s3d_preprocess/README.md

Structured3D preprocessing for floorplan data

We thank the authors of MonteFloor for providing the preprocessing scripts to generate the floorplan data from Structured3D dataset.

We prepare the training data for HEAT based on the generated density/normal images and the raw floorplan annotations. Note that all the data used in our paper can be downloaded from our links, and this readme doc is an inexhaustive explanation for those who interested in the data preprocessing process.

Generate floorplan data (the original readme provided by MonteFloor)

This code is based on Structured3D repository.

To generate floorplans, run generate_floors.py script:

python generate_floors.py

Prior to that, you should modify path variables in DataProcessing.FloorRW. (daataset_path, and mode)

Some scenes have missing/wrong annotations. These are the indices that you should additionally exclude from test set:

wrong_s3d_annotations_list = [3261, 3271, 3276, 3296, 3342, 3387, 3398, 3466, 3496]

Generate the training annotations for HEAT

In HEAT's formulation, each floorplan is represented by a planar graph. However, the raw annotations from Structured3D represent the floorplan by a list of closed loops. To prepare the ground-truth training data for HEAT, we need to further process the raw annotations to get proper planar graphs. We refer to the room merging step of Floor-SP and implement a merging algorithm (in generate_planar_graph.py) to generate planar graphs from the raw annotations.

Note: the generated planar graphs are only used for training HEAT. For evaluation, we extract the rooms from the estimtaed planar graph as closed loops and follow the original evaluation pipeline established by MonteFloor. Check the quantitative evaluation section for the details.

Please run the script generate_planar_graph.py to merge the rooms and get the training annotations for HEAT.