InspectionMLviewpointGen

Requires python 3.10.0

Dependencies required to run Bayesian Segmentation Jupiter notebook

Install dependencies.

pip3 install open3d==0.17.0 scikit-learn pytransform3d numpy bayesian-optimization ipykernel

To run the Bayesian Segmentation

Open bayesian_segmentation.ipynb in src folder and follow the instructions on the python notebook

Working

• 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: