Skip to content

Latest commit

 

History

History
60 lines (40 loc) · 2.63 KB

data.md

File metadata and controls

60 lines (40 loc) · 2.63 KB

Importing data

Capturing your own datasets

If you have a LiDAR enabled iOS device, you can use the Stray Scanner app to record data. The script at scripts/convert_scanner.py will allow you to convert a scene recorded using the app to the above format. You can then run the mapping.py script to run structure from motion and compute the other outputs.

After capturing and moving the scenes over to your computer, convert to Autolabel format with:

python scripts/convert_scanner.py <scanner-scene> --out scenes/scene_name/

# Compute camera poses.
python scripts/mapping.py scenes/scene_name/

Importing ARKitScenes scenes

Here are the steps required to download and import scenes from the ARKitScenes dataset.

# Clone ARKitScenes repository
git clone https://github.com/apple/ARKitScenes.git arkit-scenes
cd arkit-scenes

# Create a directory for the scenes to download them
mkdir -p scenes/raw/ && mkdir -p scenes/converted

# Download the required parts of the dataset
# For now, we only download the low resolution RGB images (256x192), but higher
# resolution frames could be used.
python download_data.py raw --split Training \
  --video_id_csv depth_upsampling/upsampling_train_val_splits.csv \
  --download_dir scenes/raw \
  --raw_dataset_assets lowres_wide lowres_depth lowres_wide.traj confidence lowres_wide_intrinsics

# Convert the ARKitScenes to the format used by Autolabel
python scripts/convert_arkitscenes.py scenes/raw/ --out scenes/converted/

Importing Replica renders

We have written data conversion scripts for different publicly available datasets.

Renders from the Replica dataset published by SemanticNeRF can be converted using the scripts/convert_replica.py script.

Importing ScanNet scenes

ScanNet scenes can be imported with the scripts/convert_scannet.py script.

It is used as:

python scripts/convert_scannet.py <scannet-directory> --label-map <path-to-labelmap.tsv> --out <path-to-output-directory> --stride <N>
  • scannet-directory this is the path to the raw scannet dataset which contains each scene as a subdirectory. Each scene in turn contains the *-label-filt.zip, *.sens etc. files in the ScanNet format.
  • --out specifies the output directory. Each scene will be stored as a subdirectory.
  • --stride is an integer parameter specifying how many frames to keep. Only every Nth frame is kept in the scan.

This will in addition also create mesh.ply and mesh_labels.npy files use by the vision-language evaluation scripts.