Point clouds are an important type of geometric data structure. OpenVINO can directly consume point cloud data and perform inference with it.
This notebook demonstrates how to process point cloud data and run 3D Part Segmentation with OpenVINO. The inputs of this task are a collection of individual data points in a three-dimensional plane with each point having a set coordinates on the X, Y, and Z axes.
In this notebook, we use the PointNet pre-trained model to detect each part of a chair and return its category.
If you have not done so already, please follow the Installation Guide to install all required dependencies.