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.