PointCloudConfig

Configuration Options

• Sparsity: Determines whether the generated point cloud should be sparse. Sparse point clouds may omit points based on certain criteria, such as depth value thresholds, to reduce data volume and processing requirements.
• Transformation Matrix: Applies a transformation matrix to the point cloud data, enabling rotations, translations, and scaling to align the point cloud with a world or application-specific coordinate system.

Usage

1import depthai as dai
2
3# Create pipeline
4pipeline = dai.Pipeline()
5
6# Create PointCloud node
7pointCloud = pipeline.create(dai.node.PointCloud)
8
9pointCloud.initialConfig.setSparse(True) # Enable sparse point cloud generation
10
11# Define a transformation matrix
12transformationMatrix = [
13    [1.0, 0.0, 0.0, 0.0],
14    [0.0, 1.0, 0.0, 0.0],
15    [0.0, 0.0, 1.0, 0.0],
16    [0.0, 0.0, 0.0, 1.0]
17]
18pointCloud.initialConfig.setTransformationMatrix(transformationMatrix) # Apply transformation matrix
19
20# Further pipeline setup and execution...

Examples of Functionality

• 3D Object Localization: Adjusting the transformation matrix to align point clouds with a known coordinate system for precise object placement.
• Scene Optimization: Utilizing sparse point clouds for efficient processing in large-scale or complex scenes.
• Data Alignment: Applying transformation matrices for seamless integration of point cloud data with other sensor data or pre-existing 3D models.

Reference