https://github.com/kuixu/kitti_object_vis
KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )
https://github.com/kuixu/kitti_object_vis
3d-object-detection birdview kitti-dataset lidar-point-cloud visualization
Last synced: 20 days ago
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KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )
- Host: GitHub
- URL: https://github.com/kuixu/kitti_object_vis
- Owner: kuixu
- License: mit
- Created: 2018-06-28T06:02:31.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-02-04T07:01:45.000Z (about 3 years ago)
- Last Synced: 2025-04-12T06:15:09.421Z (20 days ago)
- Topics: 3d-object-detection, birdview, kitti-dataset, lidar-point-cloud, visualization
- Language: Jupyter Notebook
- Size: 11 MB
- Stars: 1,102
- Watchers: 14
- Forks: 235
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# KITTI Object data transformation and visualization
## Dataset
Download the data (calib, image\_2, label\_2, velodyne) from [Kitti Object Detection Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) and place it in your data folder at `kitti/object`
The folder structure is as following:
```
kitti
object
testing
calib
000000.txt
image_2
000000.png
label_2
000000.txt
velodyne
000000.bin
pred
000000.txt
training
calib
000000.txt
image_2
000000.png
label_2
000000.txt
velodyne
000000.bin
pred
000000.txt
```
## Install locally on a Ubuntu 16.04 PC with GUI
- start from a new conda enviornment:
```
(base)$ conda create -n kitti_vis python=3.7 # vtk does not support python 3.8
(base)$ conda activate kitti_vis
```
- opencv, pillow, scipy, matplotlib
```
(kitti_vis)$ pip install opencv-python pillow scipy matplotlib
```
- install mayavi from conda-forge, this installs vtk and pyqt5 automatically
```
(kitti_vis)$ conda install mayavi -c conda-forge
```
- test installation
```
(kitti_vis)$ python kitti_object.py --show_lidar_with_depth --img_fov --const_box --vis
```
**Note: the above installation has been tested not work on MacOS.**
## Install remotely
Please refer to the [jupyter](jupyter/) folder for installing on a remote server and visulizing in Jupyter Notebook.
## 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
```shell
$ 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]
KIITI Object Visualization
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)
```
```shell
$ python kitti_object.py
```
Specific your own folder,
```shell
$ 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 1
```
Show LiDAR with `modified LiDAR file` with an additional point cloud label/marker as the 5th dimention(5 vector: x, y, z, intensity, pc_label). (This option is for very specific case. If you don't have this type of data, don't use this option).
```
$ 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
Credit: @yuanzhenxun
#### 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
python kitti_object.py -p --vis
```
## Acknowlegement
Code is mainly from [f-pointnet](https://github.com/charlesq34/frustum-pointnets) and [MV3D](https://github.com/bostondiditeam/MV3D)