KITTI Dataset¶

This page provides specific tutorials about the usage of MMDetection3D for KITTI dataset.

Prepare dataset¶

You can download KITTI 3D detection data HERE and unzip all zip files. Besides, the road planes could be downloaded from HERE, which are optional for data augmentation during training for better performance. The road planes are generated by AVOD, you can see more details HERE.

Like the general way to prepare dataset, it is recommended to symlink the dataset root to $MMDETECTION3D/data.

The folder structure should be organized as follows before our processing.

mmdetection3d
├── mmdet3d
├── tools
├── configs
├── data
│   ├── kitti
│   │   ├── ImageSets
│   │   ├── testing
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── velodyne
│   │   ├── training
│   │   │   ├── calib
│   │   │   ├── image_2
│   │   │   ├── label_2
│   │   │   ├── velodyne
│   │   │   ├── planes (optional)

Create KITTI dataset¶

To create KITTI point cloud data, we load the raw point cloud data and generate the relevant annotations including object labels and bounding boxes. We also generate all single training objects’ point cloud in KITTI dataset and save them as .bin files in data/kitti/kitti_gt_database. Meanwhile, .pkl info files are also generated for training or validation. Subsequently, create 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 --with-plane

Note that if your local disk does not have enough space for saving converted data, you can change the --out-dir to anywhere else, and you need to remove the --with-plane flag if planes are not prepared.

The folder structure after processing should be as below

kitti
├── ImageSets
│   ├── test.txt
│   ├── train.txt
│   ├── trainval.txt
│   ├── val.txt
├── testing
│   ├── calib
│   ├── image_2
│   ├── velodyne
│   ├── velodyne_reduced
├── training
│   ├── calib
│   ├── image_2
│   ├── label_2
│   ├── velodyne
│   ├── velodyne_reduced
│   ├── planes (optional)
├── kitti_gt_database
│   ├── xxxxx.bin
├── kitti_infos_train.pkl
├── kitti_infos_val.pkl
├── kitti_dbinfos_train.pkl
├── kitti_infos_test.pkl
├── kitti_infos_trainval.pkl

kitti_gt_database/xxxxx.bin: point cloud data included in each 3D bounding box of the training dataset.
kitti_infos_train.pkl: training dataset, a dict contains two keys: metainfo and data_list. metainfo contains the basic information for the dataset itself, such as categories, dataset and info_version, while data_list is a list of dict, each dict (hereinafter referred to as info) contains all the detailed information of single sample as follows:
- info[‘sample_idx’]: The index of this sample in the whole dataset.
- info[‘images’]: Information of images captured by multiple cameras. A dict contains five keys including: CAM0, CAM1, CAM2, CAM3, R0_rect.
  - info[‘images’][‘R0_rect’]: Rectifying rotation matrix with shape (4, 4).
  - info[‘images’][‘CAM2’]: Include some information about the CAM2 camera sensor.
    - info[‘images’][‘CAM2’][‘img_path’]: The filename of the image.
    - info[‘images’][‘CAM2’][‘height’]: The height of the image.
    - info[‘images’][‘CAM2’][‘width’]: The width of the image.
    - info[‘images’][‘CAM2’][‘cam2img’]: Transformation matrix from camera to image with shape (4, 4).
    - info[‘images’][‘CAM2’][‘lidar2cam’]: Transformation matrix from lidar to camera with shape (4, 4).
    - info[‘images’][‘CAM2’][‘lidar2img’]: Transformation matrix from lidar to image with shape (4, 4).
- info[‘lidar_points’]: A dict containing all the information related to the lidar points.
  - info[‘lidar_points’][‘lidar_path’]: The filename of the lidar point cloud data.
  - info[‘lidar_points’][‘num_pts_feats’]: The feature dimension of point.
  - info[‘lidar_points’][‘Tr_velo_to_cam’]: Transformation from Velodyne coordinate to camera coordinate with shape (4, 4).
  - info[‘lidar_points’][‘Tr_imu_to_velo’]: Transformation from IMU coordinate to Velodyne coordinate with shape (4, 4).
- info[‘instances’]: It is a list of dict. Each dict contains all annotation information of single instance. For the i-th instance:
  - info[‘instances’][i][‘bbox’]: List of 4 numbers representing the 2D bounding box of the instance, in (x1, y1, x2, y2) order.
  - info[‘instances’][i][‘bbox_3d’]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, l, h, w, yaw) order.
  - info[‘instances’][i][‘bbox_label’]: An int indicate the 2D label of instance and the -1 indicating ignore.
  - info[‘instances’][i][‘bbox_label_3d’]: An int indicate the 3D label of instance and the -1 indicating ignore.
  - info[‘instances’][i][‘depth’]: Projected center depth of the 3D bounding box with respect to the image plane.
  - info[‘instances’][i][‘num_lidar_pts’]: The number of LiDAR points in the 3D bounding box.
  - info[‘instances’][i][‘center_2d’]: Projected 2D center of the 3D bounding box.
  - info[‘instances’][i][‘difficulty’]: KITTI difficulty: ‘Easy’, ‘Moderate’, ‘Hard’.
  - info[‘instances’][i][‘truncated’]: Float from 0 (non-truncated) to 1 (truncated), where truncated refers to the object leaving image boundaries.
  - info[‘instances’][i][‘occluded’]: Integer (0,1,2,3) indicating occlusion state: 0 = fully visible, 1 = partly occluded, 2 = largely occluded, 3 = unknown.
  - info[‘instances’][i][‘group_ids’]: Used for multi-part object.
- info[‘plane’](optional): Road level information.

Please refer to kitti_converter.py and update_infos_to_v2.py for more details.

Train pipeline¶

A typical train pipeline of 3D detection on KITTI is as below:

train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=4, # x, y, z, intensity
        use_dim=4),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(type='ObjectSample', db_sampler=db_sampler),
    dict(
        type='ObjectNoise',
        num_try=100,
        translation_std=[1.0, 1.0, 0.5],
        global_rot_range=[0.0, 0.0],
        rot_range=[-0.78539816, 0.78539816]),
    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.78539816, 0.78539816],
        scale_ratio_range=[0.95, 1.05]),
    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='PointShuffle'),
    dict(
        type='Pack3DDetInputs',
        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]

Data augmentation:
- ObjectNoise: apply noise to each GT objects in the scene.
- RandomFlip3D: randomly flip input point cloud horizontally or vertically.
- GlobalRotScaleTrans: rotate input point cloud.

Evaluation¶

An example to evaluate PointPillars with 8 GPUs with kitti metrics is as follows:

bash tools/dist_test.sh configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py work_dirs/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class/latest.pth 8

Metrics¶

KITTI evaluates 3D object detection performance using mean Average Precision (mAP) and Average Orientation Similarity (AOS), Please refer to its official website and original paper for more details.

We also adopt this approach for evaluation on KITTI. An example of printed evaluation results is as follows:

Car AP@0.70, 0.70, 0.70:
bbox AP:97.9252, 89.6183, 88.1564
bev  AP:90.4196, 87.9491, 85.1700
3d   AP:88.3891, 77.1624, 74.4654
aos  AP:97.70, 89.11, 87.38
Car AP@0.70, 0.50, 0.50:
bbox AP:97.9252, 89.6183, 88.1564
bev  AP:98.3509, 90.2042, 89.6102
3d   AP:98.2800, 90.1480, 89.4736
aos  AP:97.70, 89.11, 87.38

Testing and make a submission¶

An example to test PointPillars on KITTI with 8 GPUs and generate a submission to the leaderboard is as follows:

First, you need to modify the test_dataloader and test_evaluator dict in your config file, just like:

data_root = 'data/kitti/'
test_dataloader = dict(
    dataset=dict(
        ann_file='kitti_infos_test.pkl',
        load_eval_anns=False,
        data_prefix=dict(pts='testing/velodyne_reduced')))
test_evaluator = dict(
    ann_file=data_root + 'kitti_infos_test.pkl',
    format_only=True,
    pklfile_prefix='results/kitti-3class/kitti_results',
    submission_prefix='results/kitti-3class/kitti_results')

And then, you can run the test script.

./tools/dist_test.sh configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py work_dirs/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class/latest.pth 8

After generating results/kitti-3class/kitti_results/xxxxx.txt files, you can submit these files to KITTI benchmark. Please refer to the KITTI official website for more details.