// Point Cloud Processing Steps:

1. Acquisition
   - Capture raw depth frames from 3D sensor
   - Timestamp synchronization with RGB frames

2. Pre-processing
   - Remove invalid points (confidence threshold)
   - Multi-path interference correction
   - Flying pixel removal

3. Filtering
   - Statistical outlier removal
   - Voxel grid downsampling
   - Pass-through filtering (range limits)

4. Transformation
   - Camera to world coordinate transform
   - Sensor calibration application
   - Multi-sensor registration

5. Feature Extraction
   - Normal estimation
   - Keypoint detection (SIFT, SHOT, etc.)
   - Surface reconstruction

6. Application
   - Object detection and tracking
   - SLAM (Simultaneous Localization and Mapping)
   - 3D reconstruction
   - Gesture recognition
                    

import { ThreeDImageSensor, ToFSensor, DepthFrame } from 'wia-3d-sensor-sdk';

// Initialize 3D sensor
const sensor = new ThreeDImageSensor({
  type: 'iToF',
  resolution: { width: 640, height: 480 },
  frameRate: 30,
  depthRange: { min: 0.1, max: 10 },
  accuracy: 1.0 // mm
});

// Start depth streaming
await sensor.start();

// Capture depth frame
sensor.on('depth-frame', (frame: DepthFrame) => {
  console.log(`Frame ${frame.id}`);
  console.log(`Timestamp: ${frame.timestamp}`);
  console.log(`Depth data points: ${frame.points.length}`);

  // Process point cloud
  const pointCloud = frame.toPointCloud();

  // Apply filters
  pointCloud
    .removeOutliers({ threshold: 2.0 })
    .downsample({ voxelSize: 0.01 })
    .transform(cameraToWorldMatrix);

  // Export
  const pcdData = pointCloud.export('pcd');
});

// Face detection integration
const faceDetector = sensor.createFaceDetector({
  minDistance: 0.3,
  maxDistance: 1.2,
  depthThreshold: 0.05
});

faceDetector.on('face-detected', (face) => {
  console.log('Face detected:', face.boundingBox);
  console.log('Depth map:', face.depthMap);
});