System Dependencies and Integrations

This document provides a comprehensive overview of all dependencies, integrations, and packages used across different systems within the KAI platform. It serves as a reference for developers and should be updated whenever new dependencies are added.

Table of Contents

• 3D Visualization & Model Generation
• Material Recognition & Property Extraction
• Scene Understanding & Reconstruction
• Interior Design & Automation
• Texture & Content Generation
• Mobile & Performance Optimization
• Infrastructure & Processing
• Development Tools

3D Visualization & Model Generation

Dependency	Version	Purpose	Integration Points
hdrnet-pytorch	^0.2.0	Automatic lighting inference and environment map generation	Used by LightingEstimationService to generate HDR environment maps for realistic lighting in 3D visualizations
envmapnet	^0.1.0	Environment map processing and enhancement	Complements HDRNet for improved environment map quality and tone mapping
Three.js	N/A	3D rendering library for web-based visualization	Core component for all 3D visualization features in client applications

Key Components:

• LightingEstimationService: Leverages HDRNet for automatic lighting inference from images
• MaterialVisualizationProvider: Uses environment maps for physically-based lighting in 3D scenes
• ThreeJsViewer: Renders 3D models with realistic lighting and materials

Integration Flow:

1. Material images are processed through HDRNet to extract lighting information
2. Generated environment maps are used for physically-based rendering
3. Three.js creates the final visualization with accurate lighting and shadows

Material Recognition & Property Extraction

Dependency	Version	Purpose	Integration Points
materialnet	^1.0.0	Automatic PBR material property extraction	Used by MaterialNetProvider to extract physically-based rendering properties from material images
tensorflow-js	N/A	ML inference for front-end material recognition	Powers client-side material recognition features
pytorch	N/A	Deep learning framework for advanced material analysis	Backend for material recognition model training and inference

Key Components:

• MaterialNetProvider: Extracts PBR properties from material images
• MaterialRecognitionProvider: Identifies materials from images
• MaterialVisualizationProvider: Uses extracted properties for accurate rendering

Integration Flow:

1. Material images are processed through MaterialNet to extract PBR properties
2. Extracted properties are used to enhance 3D visualizations
3. Recognition results inform material selection and recommendations

Scene Understanding & Reconstruction

Dependency	Version	Purpose	Integration Points
pycolmap	^0.3.0	Camera pose estimation and multi-view consistency	Used by CameraEstimationProvider to extract camera poses from multiple images for consistent 3D reconstruction
diffusionnerf	^0.1.0	Enhanced scene optimization with diffusion models	Used by DiffusionNeRFProvider to improve scene reconstruction with adaptive quality selection based on input images
point-e	^0.1.0	Point cloud processing, noise reduction, and geometry optimization	Used by PointCloudProvider to enhance point cloud processing prior to mesh construction, improve geometry quality, and add denoising capabilities
scene-graph-3d	^0.1.0	3D scene graph generation for relational understanding	Powers the SceneGraphProvider for creating relationship-aware scene representations, semantic queries, and intelligent scene editing tools

Key Components:

• CameraEstimationProvider: Uses COLMAP for Structure-from-Motion processing and camera pose extraction
• colmap_sfm_service.py: Python service that performs the actual COLMAP processing
• camera-pose.routes.ts: API endpoints for camera pose estimation and NeRF enhancement
• DiffusionNeRFProvider: Extends BaseThreeDProvider to optimize scene reconstruction using diffusion models
• diffusion_nerf_service.py: Python service that implements quality assessment and adaptive reconstruction methods
• scene-optimization.routes.ts: API endpoints for scene optimization and quality-based reconstruction
• NeRFProvider: Handles neural radiance field processing for 3D reconstruction
• PointCloudProvider: Processes and optimizes point cloud data with noise reduction and geometry enhancement
• point_cloud_service.py: Python service that implements point cloud processing, denoising, and optimization algorithms
• point-cloud.routes.ts: API endpoints for point cloud processing, generation, completion, and mesh improvement
• SceneGraphProvider: Generates scene graphs for semantic understanding

Integration Flow:

1. Camera Pose Estimation:

   • Multiple images are uploaded through the /api/camera-pose/estimate endpoint
   • Images are processed by the COLMAP service to extract camera poses
   • Results include camera intrinsics, extrinsics, and 3D points
   • Optional visualization and NeRF format conversion

2. NeRF Enhancement:

   • Existing NeRF data is enhanced with camera poses via the /api/camera-pose/enhance-nerf endpoint
   • Improved camera positioning leads to more accurate 3D reconstructions
   • Results are compatible with standard NeRF training pipelines

3. Scene Optimization:

   • Images are analyzed for quality assessment via the /api/scene-optimization/assess endpoint
   • Based on quality metrics, the appropriate reconstruction method is selected
   • For sparse or incomplete views, DiffusionNeRF is used to fill in missing information
   • Results include enhanced 3D models with improved geometry and textures
   • Optional caching for faster processing of similar scenes

4. 3D Visualization:

   • Camera pose data is used by the ThreeJsViewer for accurate perspective rendering
   • MaterialVisualizationProvider uses camera positions for consistent material appearance
   • DiffusionNeRFProvider generates optimized scenes for visualization
   • PointCloudProvider optimizes point clouds and improves mesh quality
   • SceneController leverages camera transformation matrices for proper navigation

5. Material Recognition Integration:

   • Camera poses provide geometric context for material recognition
   • Multiple views of the same material improve property extraction accuracy
   • DiffusionNeRF models enhance material surfaces for better property extraction
   • MaterialNetProvider benefits from spatial relationships between detected materials

6. Adaptive Processing Pipeline:

   • Quality assessment determines the best reconstruction approach:
     • High-quality inputs: Standard NeRF reconstruction
     • Medium-quality inputs: Hybrid approach with DiffusionNeRF enhancement
     • Low-quality inputs: Full DiffusionNeRF reconstruction with regularization
   • Processing results are cached for similar future requests
   • Point-E Integration:
     • Raw point clouds are processed with noise reduction via /api/point-cloud/process
     • Text-to-point-cloud generation via /api/point-cloud/generate
     • Partial point cloud completion via /api/point-cloud/complete
     • Mesh geometry improvement via /api/point-cloud/improve-mesh
     • Image-to-point-cloud extraction via /api/point-cloud/process-image
   • Scene Graph Generation:
     • SceneGraphProvider: Creates relationship-aware scene representations via /api/scene-graph/* endpoints
     • scene_graph_service.py: Python ML service that implements the 3DSSG algorithms
     • Supports multiple input types:
       • 3D model-based scene graph generation via /api/scene-graph/generate-from-model
       • Point cloud-based graph generation via /api/scene-graph/generate-from-point-cloud
       • Image-based graph generation via /api/scene-graph/generate-from-images
       • Text-based scene description via /api/scene-graph/generate-from-text
     • Advanced features:
       • Semantic queries on scene graphs via /api/scene-graph/query
       • Layout suggestions via /api/scene-graph/generate-suggestions
       • Relationship-aware editing with contextual understanding
       • Support for multiple confidence levels and relationship limits
     • Integration with other components:
       • Works with PointCloudProvider for geometry understanding
       • Enhances MaterialNetProvider with semantic material relationships
       • Improves RoomLayoutProvider with object relationship context

Interior Design & Automation

Dependency	Version	Purpose	Integration Points
spaceformer	^0.2.0	Layout generation and furniture placement	Powers automated interior design features through RoomLayoutProvider and ArchitecturalProvider

Key Components:

• RoomLayoutProvider: Generates optimized room layouts and handles furniture placement optimization using design principles
• FurniturePlacementService: Automatically places furniture in scenes based on accessibility and flow
• ArchitecturalProvider: Handles architectural elements, layout constraints, and room structure
• space_former_service.py: Python ML service that implements the core SpaceFormer functionality

Features:

• Automated room layout generation based on room dimensions and type
• Intelligent furniture placement using design principles
• Multi-criteria optimization (flow, accessibility, balance, etc.)
• Layout analysis with improvement suggestions
• Room image processing for layout detection
• Design style enforcement and consistency
• Accessibility scoring and optimization

API Endpoints:

• /api/room-layout/generate: Generate optimized room layouts
• /api/room-layout/optimize-furniture: Optimize furniture placement
• /api/room-layout/analyze: Analyze existing layouts and provide suggestions
• /api/room-layout/process-image: Process room images for layout recognition
• /api/room-layout/optimize-existing: Optimize existing layouts

Integration Flow:

1. Room dimensions and constraints are processed through SpaceFormer
2. Layout suggestions are generated based on design principles
3. Furniture is automatically placed according to the layout
4. Optimization goals can be specified (e.g., prioritize flow, accessibility, or style)
5. Results include metrics for flow, occupancy, accessibility, and design principle compliance

Texture & Content Generation

Dependency	Version	Purpose	Integration Points
text2texture	^0.1.0	Texture generation and enhancement	Improves texture quality and generates new textures from descriptions

Key Components:

• TextureEnhancementProvider: Enhances and generates textures
• TextToTextureService: Converts text descriptions to textures

Integration Flow:

1. Low-resolution textures are processed for enhancement
2. Text descriptions are converted to high-quality textures
3. Generated textures are applied to 3D models

Mobile & Performance Optimization

Dependency	Version	Purpose	Integration Points
draco3d	^1.5.0	3D mesh compression for mobile delivery	Optimizes 3D models for mobile and web delivery
assemblyscript	^0.27.1	WebAssembly compilation for browser performance	Used to compile performance-critical components to WebAssembly

Key Components:

• LODGenerator: Creates multiple detail levels for progressive loading
• MeshOptimizer: Compresses and optimizes meshes for delivery
• WasmProcessor: Handles WebAssembly compilation and execution

Integration Flow:

1. 3D models are optimized and compressed using Draco
2. Multiple LODs are generated for progressive loading
3. Performance-critical code is compiled to WebAssembly

Infrastructure & Processing

Dependency	Version	Purpose	Integration Points
serverless	^3.30.1	Serverless function deployment	Used for cloud function management
aws-lambda	^1.0.7	AWS Lambda functionality	Powers serverless processing in AWS environment
apache-airflow-client	^2.5.1	Workflow orchestration	Manages complex processing pipelines
bull	^4.10.4	Redis-based queue for Node.js	Powers distributed task processing

Key Components:

• QueueManager: Handles distributed task processing
• WorkflowManager: Orchestrates complex processing pipelines
• CloudFunctionService: Manages serverless function deployment and execution

Integration Flow:

1. Tasks are submitted to processing queues
2. Workers process tasks in distributed fashion
3. Complex workflows are orchestrated through Airflow
4. Results are stored and made available to clients

Development Tools

Dependency	Version	Purpose	Integration Points
wasm-pack	^0.10.3	WebAssembly packaging	Packages Rust code for WebAssembly
wasm-bindgen-cli	^0.2.87	WebAssembly bindings generator	Generates JavaScript bindings for WebAssembly modules

Key Components:

• BuildTools: Handles compilation and packaging
• DevEnvironment: Manages development environment setup

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Updating This Documentation

When adding new dependencies or integrations:

1. Add the dependency to the appropriate section
2. Document its purpose and integration points
3. Update any affected component descriptions
4. If creating a new category, add it to the Table of Contents

Last updated: April 2025