Requires python 3.10.0
Install dependencies.
pip3 install open3d==0.17.0 scikit-learn pytransform3d numpy==1.26.4 bayesian-optimization==2.0.2 ipykernel
Open bayesian_segmentation.ipynb in src folder and follow the instructions on the python notebook
- The python notebook takes in an STL model as input and generates segmented point cloud (each color representing a segment) satisfying the camera parameters such as the field of view and the depth of field
- The point cloud is segmented using K-means clustering as the methodology
- The clustering happens in two stages, that is first to derive the planar segments and then to divide the planar segments to fit it within the field of view
- For the first stage exponential search is used and for the second Bayesian Optimization is used to find the optimal K values
The image below shows the overall process:
For the FOV segmentation bayesian optimization the updation of cost function is shown below:
