Spatial Computing Architecture

// Spatial Computing Architecture System
interface SpatialContext {
  worldCoordinates: Matrix4;
  trackingState: TrackingState;
  spatialAnchors: SpatialAnchor[];
  occlusionMap: OcclusionData;
}

interface SpatialAnchor {
  id: string;
  position: Vector3;
  rotation: Quaternion;
  confidence: number;
  lastUpdated: number;
}

class SpatialComputingEngine {
  private renderPipeline: RenderPipeline;
  private spatialIndex: SpatialIndex;
  private trackingSystem: TrackingSystem;
  private edgeProcessor: EdgeProcessor;
  
  constructor(config: SpatialConfig) {
    this.renderPipeline = new RenderPipeline(config.renderConfig);
    this.spatialIndex = new SpatialIndex(config.indexConfig);
    this.trackingSystem = new TrackingSystem(config.trackingConfig);
    this.edgeProcessor = new EdgeProcessor(config.edgeConfig);
  }

  async initializeSpatialTracking(): Promise<SpatialContext> {
    // Initialize SLAM (Simultaneous Localization and Mapping)
    const worldMap = await this.trackingSystem.initializeSLAM();
    
    // Set up coordinate system calibration
    const calibrationData = await this.calibrateWorldCoordinates();
    
    // Create initial spatial context
    return {
      worldCoordinates: calibrationData.worldMatrix,
      trackingState: TrackingState.TRACKING,
      spatialAnchors: [],
      occlusionMap: new OcclusionData()
    };
  }

  async processFrame(
    sensorData: SensorInput,
    context: SpatialContext
  ): Promise<RenderFrame> {
    // Multi-sensor fusion for tracking
    const trackingUpdate = await this.trackingSystem.processSensorData(
      sensorData,
      context.trackingState
    );
    
    // Update spatial understanding
    context.worldCoordinates = trackingUpdate.worldMatrix;
    context.trackingState = trackingUpdate.state;
    
    // Process spatial queries and object placement
    const spatialObjects = await this.spatialIndex.query(
      trackingUpdate.frustum,
      context.spatialAnchors
    );
    
    // Handle occlusion and depth testing
    const occlusionUpdate = await this.updateOcclusion(
      sensorData.depthMap,
      spatialObjects
    );
    context.occlusionMap = occlusionUpdate;
    
    // Render frame with spatial context
    return this.renderPipeline.renderFrame({
      objects: spatialObjects,
      worldMatrix: context.worldCoordinates,
      occlusion: context.occlusionMap,
      lighting: this.calculateSpatialLighting(trackingUpdate)
    });
  }

  async createSpatialAnchor(
    position: Vector3,
    rotation: Quaternion
  ): Promise<SpatialAnchor> {
    const anchor: SpatialAnchor = {
      id: generateUUID(),
      position,
      rotation,
      confidence: 0.0,
      lastUpdated: Date.now()
    };
    
    // Validate anchor placement with environment mapping
    const validation = await this.validateAnchorPlacement(anchor);
    anchor.confidence = validation.confidence;
    
    // Persist anchor with cloud synchronization
    await this.edgeProcessor.syncAnchor(anchor);
    
    return anchor;
  }

  private async updateOcclusion(
    depthMap: DepthData,
    objects: SpatialObject[]
  ): Promise<OcclusionData> {
    // Multi-layer occlusion handling
    const occlusionLayers = await Promise.all([
      this.processPhysicalOcclusion(depthMap),
      this.processVirtualOcclusion(objects),
      this.processCrossModalOcclusion(depthMap, objects)
    ]);
    
    return this.mergeOcclusionLayers(occlusionLayers);
  }
}

// Edge Processing for Real-time Performance
class EdgeProcessor {
  private cloudConnection: CloudConnection;
  private localCache: SpatialCache;
  private processingQueue: ProcessingQueue;
  
  constructor(config: EdgeConfig) {
    this.cloudConnection = new CloudConnection(config.cloudEndpoint);
    this.localCache = new SpatialCache(config.cacheSize);
    this.processingQueue = new ProcessingQueue(config.queueSize);
  }

  async processLocalComputation(
    task: SpatialTask
  ): Promise<SpatialResult> {
    // Prioritize local processing for latency-critical tasks
    if (task.priority === Priority.REAL_TIME) {
      return this.executeLocalProcessing(task);
    }
    
    // Offload heavy computation to cloud
    if (task.computeComplexity > this.getLocalCapacity()) {
      return this.offloadToCloud(task);
    }
    
    // Hybrid processing for balanced workloads
    return this.executeHybridProcessing(task);
  }

  async syncAnchor(anchor: SpatialAnchor): Promise<void> {
    // Cache locally for immediate access
    await this.localCache.store(anchor.id, anchor);
    
    // Sync with cloud for persistence and cross-device access
    this.processingQueue.enqueue({
      type: TaskType.SYNC_ANCHOR,
      payload: anchor,
      priority: Priority.NORMAL
    });
  }

  private async executeHybridProcessing(
    task: SpatialTask
  ): Promise<SpatialResult> {
    const [localResult, cloudResult] = await Promise.allSettled([
      this.executeLocalProcessing(task),
      this.offloadToCloud(task)
    ]);
    
    // Use local result if available, fallback to cloud
    if (localResult.status === 'fulfilled') {
      return localResult.value;
    }
    
    if (cloudResult.status === 'fulfilled') {
      return cloudResult.value;
    }
    
    throw new Error('Both local and cloud processing failed');
  }
}

# Spatial Computing Data Pipeline
import numpy as np
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
import asyncio
from concurrent.futures import ThreadPoolExecutor

@dataclass
class SpatialPoint:
    x: float
    y: float
    z: float
    confidence: float
    timestamp: float

@dataclass
class SpatialRegion:
    points: List[SpatialPoint]
    bounds: Tuple[float, float, float, float, float, float]  # min_x, max_x, min_y, max_y, min_z, max_z
    density: float

class SpatialDataPipeline:
    def __init__(self, config: Dict):
        self.spatial_index = SpatialIndex(config['index_config'])
        self.point_processor = PointCloudProcessor(config['processing_config'])
        self.mesh_generator = MeshGenerator(config['mesh_config'])
        self.executor = ThreadPoolExecutor(max_workers=config.get('workers', 4))
        
    async def process_point_cloud(
        self, 
        raw_points: np.ndarray,
        sensor_metadata: Dict
    ) -> SpatialRegion:
        """Process raw point cloud data into spatial regions"""
        
        # Parallel point processing
        processed_points = await self.parallel_point_processing(
            raw_points, 
            sensor_metadata
        )
        
        # Spatial clustering and segmentation
        clusters = await self.cluster_spatial_points(processed_points)
        
        # Generate spatial regions
        regions = []
        for cluster in clusters:
            region = await self.create_spatial_region(cluster)
            if region.density > 0.5:  # Filter low-quality regions
                regions.append(region)
        
        return self.merge_overlapping_regions(regions)
    
    async def parallel_point_processing(
        self,
        raw_points: np.ndarray,
        metadata: Dict
    ) -> List[SpatialPoint]:
        """Process points in parallel for performance"""
        
        chunk_size = len(raw_points) // self.executor._max_workers
        chunks = [
            raw_points[i:i + chunk_size] 
            for i in range(0, len(raw_points), chunk_size)
        ]
        
        # Process chunks in parallel
        loop = asyncio.get_event_loop()
        tasks = [
            loop.run_in_executor(
                self.executor,
                self.process_point_chunk,
                chunk,
                metadata
            )
            for chunk in chunks
        ]
        
        results = await asyncio.gather(*tasks)
        
        # Flatten results
        processed_points = []
        for chunk_result in results:
            processed_points.extend(chunk_result)
            
        return processed_points
    
    def process_point_chunk(
        self,
        points: np.ndarray,
        metadata: Dict
    ) -> List[SpatialPoint]:
        """Process a chunk of points"""
        
        processed = []
        for point in points:
            # Apply sensor calibration
            calibrated = self.apply_sensor_calibration(point, metadata)
            
            # Calculate confidence based on sensor quality
            confidence = self.calculate_point_confidence(calibrated, metadata)
            
            # Filter low-confidence points
            if confidence > 0.3:
                processed.append(SpatialPoint(
                    x=calibrated[0],
                    y=calibrated[1], 
                    z=calibrated[2],
                    confidence=confidence,
                    timestamp=metadata['timestamp']
                ))
        
        return processed
    
    async def cluster_spatial_points(
        self,
        points: List[SpatialPoint]
    ) -> List[List[SpatialPoint]]:
        """Cluster points into spatial groups"""
        
        # Convert to numpy array for processing
        point_array = np.array([[p.x, p.y, p.z] for p in points])
        
        # Use DBSCAN clustering with spatial parameters
        from sklearn.cluster import DBSCAN
        
        clustering = DBSCAN(
            eps=0.1,  # 10cm cluster radius
            min_samples=5,  # Minimum points per cluster
            algorithm='ball_tree'  # Efficient for 3D spatial data
        ).fit(point_array)
        
        # Group points by cluster labels
        clusters = {}
        for i, label in enumerate(clustering.labels_):
            if label == -1:  # Skip noise points
                continue
            if label not in clusters:
                clusters[label] = []
            clusters[label].append(points[i])
        
        return list(clusters.values())
    
    async def create_spatial_region(
        self,
        cluster_points: List[SpatialPoint]
    ) -> SpatialRegion:
        """Create spatial region from clustered points"""
        
        if not cluster_points:
            raise ValueError("Cannot create region from empty cluster")
        
        # Calculate bounding box
        min_x = min(p.x for p in cluster_points)
        max_x = max(p.x for p in cluster_points)
        min_y = min(p.y for p in cluster_points)
        max_y = max(p.y for p in cluster_points)
        min_z = min(p.z for p in cluster_points)
        max_z = max(p.z for p in cluster_points)
        
        bounds = (min_x, max_x, min_y, max_y, min_z, max_z)
        
        # Calculate spatial density
        volume = (max_x - min_x) * (max_y - min_y) * (max_z - min_z)
        density = len(cluster_points) / max(volume, 0.001)  # Avoid division by zero
        
        return SpatialRegion(
            points=cluster_points,
            bounds=bounds,
            density=density
        )
    
    def merge_overlapping_regions(
        self,
        regions: List[SpatialRegion]
    ) -> List[SpatialRegion]:
        """Merge overlapping spatial regions"""
        
        merged = []
        processed = set()
        
        for i, region_a in enumerate(regions):
            if i in processed:
                continue
                
            merged_points = region_a.points.copy()
            processed.add(i)
            
            # Check for overlaps with remaining regions
            for j, region_b in enumerate(regions[i+1:], i+1):
                if j in processed:
                    continue
                    
                if self.regions_overlap(region_a.bounds, region_b.bounds):
                    merged_points.extend(region_b.points)
                    processed.add(j)
            
            # Create merged region
            if merged_points:
                merged_region = asyncio.run(
                    self.create_spatial_region(merged_points)
                )
                merged.append(merged_region)
        
        return merged

# Real-time Spatial Index for Fast Queries
class SpatialIndex:
    def __init__(self, config: Dict):
        self.index_type = config.get('type', 'rtree')
        self.spatial_tree = self.initialize_index(config)
        self.region_cache = {}
        
    def initialize_index(self, config: Dict):
        """Initialize spatial indexing structure"""
        if self.index_type == 'rtree':
            from rtree import index
            return index.Index()
        elif self.index_type == 'kdtree':
            from sklearn.neighbors import KDTree
            return None  # Will be built when first data is inserted
        else:
            raise ValueError(f"Unsupported index type: {self.index_type}")
    
    async def insert_region(self, region_id: str, region: SpatialRegion):
        """Insert spatial region into index"""
        bounds = region.bounds
        
        if self.index_type == 'rtree':
            self.spatial_tree.insert(
                hash(region_id), 
                bounds
            )
        
        self.region_cache[region_id] = region
    
    async def query_spatial_region(
        self,
        query_bounds: Tuple[float, float, float, float, float, float],
        max_results: int = 100
    ) -> List[SpatialRegion]:
        """Query spatial index for overlapping regions"""
        
        if self.index_type == 'rtree':
            intersecting_ids = list(
                self.spatial_tree.intersection(query_bounds)
            )[:max_results]
            
            return [
                self.region_cache[str(region_id)] 
                for region_id in intersecting_ids
                if str(region_id) in self.region_cache
            ]
        
        return []