data_prepartion.md

We follow the documents in mmdet3d and prepare the data with following file structure.

mmdetection3d
├── mmdet3d
├── tools
├── configs
├── data
│   ├── kitti
│   │   ├── ImageSets
│   │   ├── testing
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── velodyne
│   │   ├── training
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── label_2
│   │   │   ├── velodyne
|   |   ├── kitti_raw

Download the labeled 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

Download the unlabeled KITTI 3D detection data (raw split) by using the script in ``tools/data_converter/raw_kitti_downloader.sh. Download the raw split from google drive。

Then generate the info files for the unlabeled split by running