Semantic segmentation clusters the points of a 3-D point
cloud by using their similar characteristics, and associates each point with a class
label such as
You can segment a point cloud based on edges, neighboring point properties, and geometric shapes such as cuboid, plane, and cylinder. Lidar Toolbox™ includes functions and workflows for geometric segmentation of point clouds. For more information, see the Terrain Classification for Aerial Lidar Data example.
Lidar Toolbox also supports semantic segmentation using deep learning. You can use the included pretrained PointSeg, SqueezeSegV2, and PointNet++ convolutional neural networks (CNNs) or develop custom segmentation models. For a segmentation workflow using a PointNet++ network, see Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning.
|Segment ground from lidar data using a SMRF algorithm|
|Segment organized 3-D range data into clusters|
|Segment ground points from organized lidar data|
|Segment curb points from point cloud|
|Segment point cloud into clusters based on Euclidean distance|
Load Training Data
|Combine data from multiple datastores|
|Count occurrence of pixel or box labels|
|Ground truth label data|
|Datastore for image data|
|Datastore for pixel label data|
Augment and Preprocess Training Data
|Sample 3-D bounding boxes and corresponding points from training data|
|Randomly augment point cloud data using objects|
|Point cloud input layer|
|Point cloud input layer|
|Create SqueezeSegV2 segmentation network for organized lidar point cloud|
|Create PointNet++ segmentation network|
Segment Point Cloud
|Point cloud semantic segmentation using deep learning|
|Semantic image segmentation using deep learning|
|Segment vegetation points from aerial lidar data|
|Segment building points from aerial lidar data|
|Overlay label matrix regions on 2-D image|
|Plot 3-D point cloud|
|Evaluate semantic segmentation data set against ground truth|
|Confusion matrix of multi-class pixel-level image segmentation|
- Deep Learning with Point Clouds
Learn point cloud processing using deep learning.
- Semantic Segmentation in Point Clouds Using Deep Learning
Assign class labels to each point inside a point cloud using deep learning.
- Getting Started with PointNet++
Define a PointNet++ network and use it to perform semantic segmentation.
- Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
- List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB®.