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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](https://goo.gl/forms/2YSPaO2UKmn5Td5m2). 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](https://goo.gl/forms/4SoGp4KtH1jfRqEj2) 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](https://arxiv.org/abs/1507.00448) the RGB model to depth images
using paired RGB-D images. Both there models are available through
`scripts/script_download_init_models.sh`
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