Dataset Preparation¶

Before Preparation¶

It is recommended to symlink the dataset root to $MMDETECTION3D/data. If your folder structure is different from the following, you may need to change the corresponding paths in config files.

mmdetection3d
├── mmdet3d
├── tools
├── configs
├── data
│   ├── nuscenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval
│   ├── kitti
│   │   ├── ImageSets
│   │   ├── testing
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── velodyne
│   │   ├── training
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── label_2
│   │   │   ├── velodyne
│   ├── waymo
│   │   ├── waymo_format
│   │   │   ├── training
│   │   │   ├── validation
│   │   │   ├── testing
│   │   │   ├── gt.bin
│   │   ├── kitti_format
│   │   │   ├── ImageSets
│   ├── lyft
│   │   ├── v1.01-train
│   │   │   ├── v1.01-train (train_data)
│   │   │   ├── lidar (train_lidar)
│   │   │   ├── images (train_images)
│   │   │   ├── maps (train_maps)
│   │   ├── v1.01-test
│   │   │   ├── v1.01-test (test_data)
│   │   │   ├── lidar (test_lidar)
│   │   │   ├── images (test_images)
│   │   │   ├── maps (test_maps)
│   │   ├── train.txt
│   │   ├── val.txt
│   │   ├── test.txt
│   │   ├── sample_submission.csv
│   ├── s3dis
│   │   ├── meta_data
│   │   ├── Stanford3dDataset_v1.2_Aligned_Version
│   │   ├── collect_indoor3d_data.py
│   │   ├── indoor3d_util.py
│   │   ├── README.md
│   ├── scannet
│   │   ├── meta_data
│   │   ├── scans
│   │   ├── scans_test
│   │   ├── batch_load_scannet_data.py
│   │   ├── load_scannet_data.py
│   │   ├── scannet_utils.py
│   │   ├── README.md
│   ├── sunrgbd
│   │   ├── OFFICIAL_SUNRGBD
│   │   ├── matlab
│   │   ├── sunrgbd_data.py
│   │   ├── sunrgbd_utils.py
│   │   ├── README.md

Download and Data Preparation¶

KITTI¶

Download KITTI 3D detection data HERE. Prepare KITTI data splits by running:

mkdir ./data/kitti/ && mkdir ./data/kitti/ImageSets

# Download data split
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/test.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/test.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/train.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/train.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/val.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/val.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/trainval.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/trainval.txt

Then generate info files by running:

python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitti --extra-tag kitti

In an environment using slurm, users may run the following command instead:

sh tools/create_data.sh <partition> kitti

Waymo¶

Download Waymo open dataset V1.2 HERE and its data split HERE. Then put .tfrecord files into corresponding folders in data/waymo/waymo_format/ and put the data split .txt files into data/waymo/kitti_format/ImageSets. Download ground truth .bin file for validation set HERE and put it into data/waymo/waymo_format/. A tip is that you can use gsutil to download the large-scale dataset with commands. You can take this tool as an example for more details. Subsequently, prepare waymo data by running:

python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/waymo/ --workers 128 --extra-tag waymo

Note that:

If your local disk does not have enough space for saving converted data, you can change the --out-dir to anywhere else. Just remember to create folders and prepare data there in advance and link them back to data/waymo/kitti_format after the data conversion.

If you want faster evaluation on Waymo, you can download the preprocessed metainfo containing contextname and timestamp to the directory data/waymo/waymo_format/. Then, the dataset config is modified like the following:

val_evaluator = dict(
    type='WaymoMetric',
    ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
    waymo_bin_file='./data/waymo/waymo_format/gt.bin',
    data_root='./data/waymo/waymo_format',
    backend_args=backend_args,
    convert_kitti_format=True,
    idx2metainfo='data/waymo/waymo_format/idx2metainfo.pkl'
    )

Now, this trick is only used for LiDAR-based detection methods.

NuScenes¶

Download nuScenes V1.0 full dataset data HERE. Prepare nuscenes data by running:

python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

Lyft¶

Download Lyft 3D detection data HERE. Prepare Lyft data by running:

python tools/create_data.py lyft --root-path ./data/lyft --out-dir ./data/lyft --extra-tag lyft --version v1.01
python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder ./data/lyft

Note that we follow the original folder names for clear organization. Please rename the raw folders as shown above. Also note that the second command serves the purpose of fixing a corrupted lidar data file. Please refer to the discussion for more details.

S3DIS, ScanNet and SUN RGB-D¶

To prepare S3DIS data, please see its README.

To prepare ScanNet data, please see its README.

To prepare SUN RGB-D data, please see its README.

Customized Datasets¶

For using custom datasets, please refer to Customize Datasets.

Update data infos¶

If you have used v1.0.0rc1-v1.0.0rc4 mmdetection3d to create data infos before, and now you want to use the newest v1.1.0 mmdetection3d, you need to update the data infos file.

python tools/dataset_converters/update_infos_to_v2.py --dataset ${DATA_SET} --pkl-path ${PKL_PATH} --out-dir ${OUT_DIR}

--dataset : Name of dataset.
--pkl-path : Specify the data infos pkl file path.
--out-dir : Output direction of the data infos pkl file.

Example:

python tools/dataset_converters/update_infos_to_v2.py --dataset kitti --pkl-path ./data/kitti/kitti_infos_trainval.pkl --out-dir ./data/kitti