README.md

Prepare ScanNet++ Data for Indoor 3D Detection

1. Download data from the official ScanNet++.

2. Preprocess raw data by running:

python preprocess_raw_data.py --path_to_data path_to_dataset --output_dir path_to_save_preprocessed_raw_data

3. Generate bins and pkls data by running:

python prepare_bins_pkls.py --path_to_data path_to_preprocessed_raw_data --path_to_save_bins path_to_save_bins

Overall you achieve the following file structure in bins directory:

bins
├── bboxs
│   ├── xxxxx_xx.npy
├── instance_mask
│   ├── xxxxx_xx.bin
├── points
│   ├── xxxxx_xx.bin
├── semantic_mask
│   ├── xxxxx_xx.bin
├── superpoints
│   ├── xxxxx_xx.bin
├── scannetpp_infos_train.pkl
├── scannetpp_infos_val.pkl
├── scannetpp_infos_test.pkl