This directory contains the data needed for training and benchmarking various navigation models.

1. Download the data from the [dataset website] (http://buildingparser.stanford.edu/dataset.html).

   1. Raw meshes. We need the meshes which are in the noXYZ folder. Download the tar files and place them in the stanford_building_parser_dataset_raw folder. You need to download area_1_noXYZ.tar, area_3_noXYZ.tar, area_5a_noXYZ.tar, area_5b_noXYZ.tar, area_6_noXYZ.tar for training and area_4_noXYZ.tar for evaluation.
   2. Annotations for setting up tasks. We will need the file called Stanford3dDataset_v1.2.zip. Place the file in the directory stanford_building_parser_dataset_raw.

2. Preprocess the data.

   1. Extract meshes using scripts/script_preprocess_meshes_S3DIS.sh. After this ls data/stanford_building_parser_dataset/mesh should have 6 folders area1, area3, area4, area5a, area5b, area6, with textures and obj files within each directory.
   2. Extract out room information and semantics from zip file using scripts/script_preprocess_annoations_S3DIS.sh. After this there should be room-dimension and class-maps folder in data/stanford_building_parser_dataset. (If you find this script to crash because of an exception in np.loadtxt while processing Area_5/office_19/Annotations/ceiling_1.txt, there is a special character on line 323474, that should be removed manually.)

3. Download ImageNet Pre-trained models. We used ResNet-v2-50 for representing images. For RGB images this is pre-trained on ImageNet. For Depth images we distill the RGB model to depth images using paired RGB-D images. Both there models are available through scripts/script_download_init_models.sh