fvilmos/object_classification_2d_to_3d

Images from an RGB-D camera are used to detect/classify objects in 2D, then detections are projected on the 3D point cloud.

Object classification from 2D images to 3D point cloud

This approach uses the available methods for image object classification in 2D then projects the results based on RGB-D depth information to the 3D space. Object classification (in this case) is made with Cascade classifier, but easily can be changed with a Deep Neuronal Network.

Processig steps:

1. RGB and Depth information is loaded

2. RGB image is applied to a Cascade classifier. Results are filtered with Non-Maximum-Suppression

3. Object coordinated are projected in 3D space using Kinect claibration parameters. Detection are plotted on the 3D Point Cloud

Note: for visualization purposes, the pyvista and itk viewer is used. Please follow the installation instructions: https://github.com/InsightSoftwareConsortium/itkwidgets

More details on the implementation, algorithms can be found here:

• https://github.com/fvilmos/kinect_point_cloud - visualization of Kinect 3d data
• https://github.com/fvilmos/cascade_tools - train your cascade
• https://github.com/fvilmos/cascade_nms - false positive filtering with Non-Maximum-Suppression

TODO

• Optimize Classifier - change with i.e. Yolo or Mobilenet
• with a good classifier optimize away the Non-Maximum-Suppression step

Any contribution is welcomed!

Resources

1. Cascade_tools
2. Non-Maximum-Suppression - cascade

/Enjoy.