KITTI Object data transformation and visualization

Dataset

Download the data (calib, image_2, label_2, velodyne) from Kitti Object Detection Dataset and place it in your data folder at kitti/object

The folder structure is as following:

kitti
    object
        testing
            calib
            image_2
            label_2
            velodyne
        training
            calib
            image_2
            label_2
            velodyne

Requirements

• opencv

conda install opencv -c menpo

• mayavi, vtk, PIL

pip install mayavi vtk pillow
# or
conda install mayavi vtk pillow

Visualization

1. 3D boxes on LiDar point cloud in volumetric mode
2. 2D and 3D boxes on Camera image
3. 2D boxes on LiDar Birdview
4. LiDar data on Camera image

$ python kitti_object.py --help
usage: kitti_object.py [-h] [-d N] [-i N] [-p] [-s] [-l N] [-e N] [-r N]
                       [--gen_depth] [--vis] [--depth] [--img_fov]
                       [--const_box] [--save_depth] [--pc_label]
                       [--show_lidar_on_image] [--show_lidar_with_depth]
                       [--show_image_with_boxes]
                       [--show_lidar_topview_with_boxes]

PyTorch Training RPN

optional arguments:
  -h, --help            show this help message and exit
  -d N, --dir N         input (default: data/object)
  -i N, --ind N         input (default: data/object)
  -p, --pred            show predict results
  -s, --stat            stat the w/h/l of point cloud in gt bbox
  -l N, --lidar N       velodyne dir (default: velodyne)
  -e N, --depthdir N    depth dir (default: depth)
  -r N, --preddir N     predicted boxes (default: pred)
  --gen_depth           generate depth
  --vis                 show images
  --depth               load depth
  --img_fov             front view mapping
  --const_box           constraint box
  --save_depth          save depth into file
  --pc_label            5-verctor lidar, pc with label
  --show_lidar_on_image
                        project lidar on image
  --show_lidar_with_depth
                        --show_lidar, depth is supported
  --show_image_with_boxes
                        show lidar
  --show_lidar_topview_with_boxes
                        show lidar topview
  --split               use training split or testing split (default: training)

$ python kitti_object.py

Specific your own folder,

$ python kitti_object.py -d /path/to/kitti/object

Show LiDAR only

$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis

Show LiDAR and image

$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis --show_image_with_boxes

Show LiDAR and image with specific index

$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis --show_image_with_boxes --ind 100 

Show LiDAR with label (5 vector)

$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis --pc_label

Demo

2D, 3D boxes and LiDar data on Camera image

LiDar birdview and point cloud (3D)

Show Predicted Results

Firstly, map KITTI official formated results into data directory

./map_pred.sh /path/to/results

python kitti_object.py -p

Acknowlegement

Code is mainly from f-pointnet and MV3D