A basic implementation of KinectFusion [1] in Python inspired by https://github.com/andyzeng/tsdf-fusion-python.
It's light and fast (25~30 FPS with my RTX3090).
It has been tested on tabletop scenes for robot manipulation and small rooms for navigation :)
- Python >= 3.10
- CuPy (check the installation guide on the official website)
- Open3D (pip install open3d)
- cupoch (pip install cupoch)
git clone git@github.com:shiyoung77/KinectFusion-python.git
python -m KinectFusion-python.main --dataset {dataset_path} --video {video_name}
If you want to save not only the poses and reconstructed point cloud but also the TSDF volume, add --save_tsdf option.
By default it saves the result in the video folder. You can specify the output directory with --output_dir option.
Check main.py for more command line options.
Check the kf_config.py for the default parameters for reconstruction.
You should have your dataset in the following format.
{dataset_path}/
{video_name}/
color/
0000-color.png(or jpg)
0001-color.png
...
depth/
0000-depth.png
0001-depth.png
...
config.json
{second_video_name}/
...
config.json should contain the camera information. An example config.json is as follows.
{
"id": "video0",
"im_w": 640,
"im_h": 480,
"depth_scale": 1000,
"cam_intr": [
[
1066.778,
0,
312.9869
],
[
0,
1067.487,
241.3109
],
[
0,
0,
1
]
]
}
An example RGB-D video capture at Rutgers CS Robotics Lab could be found at this google drive link.
[1] Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., ... & Fitzgibbon, A. (2011, October). Kinectfusion: Real-time dense surface mapping and tracking. In 2011 10th IEEE international symposium on mixed and augmented reality (pp. 127-136). IEEE.