CloudForge - Scan-to-BIM Point Cloud Toolkit

A modular, scalable toolkit for processing point cloud data and extracting Building Information Modeling (BIM) elements from architectural scans.

🎯 Project Vision

Transform scattered point cloud processing scripts into a unified, intelligent system that automates scan-to-BIM workflows for architects and construction professionals.

✨ Features

Current (Phase 1 - Foundation)

• ✅ Multi-format Support: PLY, PCD, LAS, LAZ, E57, PTS file formats
• ✅ Scanner Presets: Pre-configured settings for Leica RTC360, FARO Focus, and more
• ✅ YAML Configuration: Flexible, human-readable configuration system
• ✅ Progress Tracking: Visual progress bars and performance monitoring
• ✅ CLI Interface: Command-line tool with scanner-specific presets
• ✅ Usage Analytics: Built-in tracking for performance optimization

Planned (Phase 2 & 3)

• 🔄 Intelligent Cleaning: Adaptive outlier removal and noise reduction
• 🔄 Glass/Reflection Detection: Advanced algorithms for mirror and glass surfaces
• 🔄 BIM Element Extraction: Automated wall, floor, ceiling detection
• 🔄 MEP System Detection: Pipes, ducts, and mechanical system identification
• 🔄 Web Visualization: Three.js-based interactive viewer
• 🔄 Quality Assurance: Alignment validation with heatmap visualization

🚀 Quick Start

Installation

# Clone the repository
git clone <repository-url>
cd pointCloud

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install additional libraries for full functionality
pip install open3d laspy  # For complete format support

Basic Usage

# List available scanner presets
python tools/cloudforge-cli/cloudforge.py list-presets

# Process a point cloud with a preset
python tools/cloudforge-cli/cloudforge.py process scan.ply --preset leica_rtc360

# Get information about a point cloud
python tools/cloudforge-cli/cloudforge.py info scan.ply

# Create a custom scanner preset
python tools/cloudforge-cli/cloudforge.py create-preset \
    --name "my_scanner" \
    --scanner "Custom Scanner Model" \
    --noise 3.0

📋 Architecture

Component Structure

components/
├── core/
│   ├── io/                     # File I/O operations
│   │   ├── multi_format_loader.py
│   │   └── export_manager.py
│   ├── config/                 # Configuration management
│   │   └── config_manager.py
│   └── utils/                  # Utilities and tracking
│       └── progress_tracker.py
├── pointcloud/                 # Processing modules
│   ├── cleaning/              # Outlier removal, noise reduction
│   ├── optimization/          # Thinning, voxel sampling
│   ├── analysis/              # Reflection detection, alignment
│   └── extraction/            # BIM element detection
└── visualization/             # Viewers and quality visualization

Configuration System

Scanner-specific presets are stored in config/presets/:

# config/presets/leica_rtc360.yaml
scanner:
  name: "Leica RTC360"
  typical_noise: 0.002  # 2mm at 10m
  max_range: 130.0
  intensity_range: [0, 2048]

cleaning:
  statistical_outlier:
    neighbors: 30
    std_ratio: 1.5
  radius_outlier:
    radius: 0.05
    min_neighbors: 10

thinning:
  method: "voxel"
  voxel_size: 0.01  # 1cm for architectural scans
  preserve_boundaries: true

reflection:
  intensity_available: true
  intensity_threshold: 0.95
  glass_detection: true

🔧 Development

Adding New Scanner Presets

1. Option 1: Use CLI

   python tools/cloudforge-cli/cloudforge.py create-preset \
       --name "new_scanner" \
       --scanner "Scanner Model Name" \
       --noise 2.5  # in millimeters

2. Option 2: Manual Configuration

   • Copy config/templates/default.yaml
   • Modify scanner parameters
   • Save as config/presets/scanner_name.yaml

Project Structure

• components/: Core functionality modules
• tools/: CLI interface and utilities
• config/: Scanner presets and templates
• tests/: Test files (to be implemented)
• templates/: Processing pipeline templates

Key Design Principles

1. Modular Components: Each processing operation is isolated and reusable
2. Configuration-Driven: All behavior controlled via YAML configuration
3. Format Agnostic: Unified interface for all point cloud formats
4. Performance First: Memory-efficient processing with progress tracking
5. Intelligence Integration: Adaptive algorithms based on scan characteristics

📊 Performance Targets

• Processing Speed: 50M+ points in < 5 minutes
• Accuracy: 95% accurate plane detection for BIM extraction
• Memory Efficiency: Batch processing for datasets > 1GB
• Quality: Automated validation and visual QA tools

🛠️ CLI Commands

Core Commands

# Process point clouds
cloudforge process <file> --preset <scanner> --output <path>

# Extract BIM elements (planned)
cloudforge extract-bim <file> --elements walls,floors --format ifc

# Quality validation (planned)
cloudforge validate <scan1> <scan2> --report alignment.html

# Configuration management
cloudforge list-presets
cloudforge create-preset --name <name> --scanner <model> --noise <mm>
cloudforge validate-config <config.yaml>

# Statistics and monitoring
cloudforge stats
cloudforge info <file>

Configuration Options

• --preset: Scanner preset (leica_rtc360, faro_focus, etc.)
• --skip-cleaning: Skip outlier removal operations
• --skip-thinning: Skip point cloud optimization
• --format: Output format (ply, pcd, pts, xyz)

📚 Supported Formats

Format	Extension	Read	Write	Notes
PLY	.ply	✅	✅	Stanford Polygon Library format
PCD	.pcd	✅	✅	Point Cloud Data format
LAS	.las/.laz	✅	❌	LIDAR data format
E57	.e57	🔄	❌	3D imaging standard (planned)
PTS	.pts	✅	✅	Simple text format
XYZ	.xyz	❌	✅	Coordinates only

🚦 Current Status

Phase 1 Complete: ✅ Foundation infrastructure

• Multi-format I/O system
• Configuration management with presets
• CLI interface with basic commands
• Progress tracking and monitoring

Phase 2 In Progress: 🔄 Intelligence layer

• Advanced cleaning algorithms
• Reflection detection
• Quality assurance tools

Phase 3 Planned: 📋 BIM integration

• Geometric primitive extraction
• Architectural element classification
• Web visualization interface

📝 Contributing

1. Fork the repository
2. Create a feature branch
3. Implement changes following the modular architecture
4. Add tests for new functionality
5. Update documentation
6. Submit a pull request

📄 License

MIT License - see LICENSE file for details

🎯 Roadmap

• Week 1-2: ✅ Core infrastructure and CLI
• Week 3-4: 🔄 Advanced processing algorithms
• Week 5-6: 📋 BIM extraction and web visualization
• Week 7+: 🚀 Performance optimization and deployment