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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 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

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

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/data_converter/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 here 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 Tutorials 2: Customize Datasets.

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