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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
│   ├── scannet
│   │   ├── meta_data
│   │   ├── scans
│   │   ├── 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

Note that we follow the original folder names for clear organization. Please rename the raw folders as shown above.

ScanNet and SUN RGB-D

To prepare scannet data, please see scannet.

To prepare sunrgbd data, please see sunrgbd.

Customized Datasets

For using custom datasets, please refer to Tutorials 2: Customize Datasets.