Shortcuts

KITTI Dataset for 3D Object Detection

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

Note: Current tutorial is only for LiDAR-based and multi-modality 3D detection methods. Contents related to monocular methods will be supplemented afterwards.

Prepare dataset

You can download KITTI 3D detection data HERE and unzip all zip files.

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

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

Note that if your local disk does not have enough space for saving converted data, you can change the out-dir to anywhere else.

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
├── 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_infos_train_mono3d.coco.json
├── kitti_infos_trainval_mono3d.coco.json
├── kitti_infos_test_mono3d.coco.json
├── kitti_infos_val_mono3d.coco.json
  • kitti_gt_database/xxxxx.bin: point cloud data included in each 3D bounding box of the training dataset

  • kitti_infos_train.pkl: training dataset infos, each frame info contains following details:

    • info[‘point_cloud’]: {‘num_features’: 4, ‘velodyne_path’: velodyne_path}.

    • info[‘annos’]: {

      • location: x,y,z are bottom center in referenced camera coordinate system (in meters), an Nx3 array

      • dimensions: height, width, length (in meters), an Nx3 array

      • rotation_y: rotation ry around Y-axis in camera coordinates [-pi..pi], an N array

      • name: ground truth name array, an N array

      • difficulty: kitti difficulty, Easy, Moderate, Hard

      • group_ids: used for multi-part object }

    • (optional) info[‘calib’]: {

      • P0: camera0 projection matrix after rectification, an 3x4 array

      • P1: camera1 projection matrix after rectification, an 3x4 array

      • P2: camera2 projection matrix after rectification, an 3x4 array

      • P2: camera2 projection matrix after rectification, an 3x4 array

      • R0_rect: rectifying rotation matrix, an 4x4 array

      • Tr_velo_to_cam: transformation from Velodyne coordinate to camera coordinate, an 4x4 array

      • Tr_imu_to_velo: transformation from IMU coordinate to Velodyne coordinate, an 4x4 array }

    • (optional) info[‘image’]:{‘image_idx’: idx, ‘image_path’: image_path, ‘image_shape’, image_shape}.

Note: the info[‘annos’] is in the referenced camera coordinate system. More details please refer to this

The core function to get kitti_infos_xxx.pkl and kitti_infos_xxx_mono3d.coco.json are get_kitti_image_info and get_2d_boxes. Please refer to kitti_converter.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, # x, y, z, intensity
        file_client_args=file_client_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        file_client_args=file_client_args),
    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='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', 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/pointrcnn/pointrcnn_2x8_kitti-3d-3classes.py work_dirs/pointrcnn_2x8_kitti-3d-3classes/latest.pth 8 --eval bbox

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:

mkdir -p results/kitti-3class

./tools/dist_test.sh configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py work_dirs/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class/latest.pth 8 --out results/kitti-3class/results_eval.pkl --format-only --eval-options 'pklfile_prefix=results/kitti-3class/kitti_results' 'submission_prefix=results/kitti-3class/kitti_results'

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.

Read the Docs v: latest
Versions
latest
stable
v0.17.1
v0.17.0
v0.16.0
v0.15.0
v0.14.0
v0.13.0
v0.12.0
v0.11.0
v0.10.0
v0.9.0
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.