Load Point Cloud Data and Corresponding Labels
Load point cloud data, load bounding box labels, and split training and testing sets.
What you learned: To load point cloud data and corresponding labels
- Load point cloud data as
- Load bounding box labels using
- Split training and testing sets
Preprocess Data Sets
Split a data set into training and test sets and discover various augmentation techniques.
What you learned: Splitting datasets and data augmentation
- Split the data set into training and test sets
- Use data augmentation for training data including:
- Randomly adding a fixed number of car and truck class objects to every point cloud
- Flipping, scaling, rotation, and translation of point cloud
Understand the definition of anchor boxes, pillars for the PointPillars network, and PointPillars network.
What you learned: To define a PointPillars network for object detection
- Define anchor boxes
- Define pillars for the PointPillars network
- Define PointPillar network
Train the model on the PointPillar network or use a pretrained model.
What you learned: To train PointPillars object detector
- Specify training options
trainPointPillarsObjectDetectorfunction to train PointPillars
- Alternatively, load a pretrained model
Use the trained network to detect objects in the test data and display the point cloud with bounding boxes.
What you learned: To test PointPillars network on test dataset
- Read the point cloud from the test data
- Run the detector on the test point cloud to get the predicted bounding boxes and confidence scores
- Display detected output point cloud with bounding boxes