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Floor Plan Segmentation Project

Table of Contents

  1. Introduction
  2. AI-ML-CV Approaches
  3. Relevant GitHub Repositories
  4. Monetizable Features
  5. Customer Base and Market
  6. Development Steps

Introduction

This project aims to develop a product for floor plan segmentation using AI, machine learning, and computer vision techniques. The goal is to create a micro SaaS solution that caters to the real estate, architecture, and interior design industries.

Goal: Convert floor-plan image into data model

AI-ML-CV Approaches

  • Image-- Edge Detection-> Vector (SVG) -- Feature Recognition -> Brep => RAG/Fine-Tuning

    • OpenCV
    • PyTorch, EdgeGAN
    • TinyML deployment

  • Convolutional Neural Networks (CNNs)

    • U-Net architecture
    • Mask R-CNN architecture

  • Instance Segmentation

  • Graph Neural Networks (GNNs)

Relevant GitHub Repositories

Note: Always verify the licenses of these repositories before using them in a commercial product.

Monetizable Features

  1. Automated room labeling and measurements
  2. 3D visualization of 2D floor plans
  3. Furniture placement recommendations
  4. Energy efficiency analysis based on floor plan layout
  5. Accessibility analysis for mobility-impaired individuals
  6. Integration with popular real estate and architecture software

Customer Base

  • Real estate agencies and property management companies
  • Architects and interior designers
  • Construction companies
  • Home improvement retailers
  • Insurance companies (for property assessment)

Development Steps

  1. Market Research

    • Conduct in-depth interviews with potential customers
    • Understand specific needs and pain points

  2. Data Collection

    • Gather diverse dataset of floor plans
    • Consider partnerships with real estate companies or architecture firms

  3. Model Development

    • Start with existing architectures (U-Net, Mask R-CNN)
    • Fine-tune models on collected dataset

  4. MVP Development

    • Create minimum viable product
    • Focus on core features like room segmentation and labeling

  5. User Testing

    • Get feedback from potential customers
    • Iterate on product based on feedback

  6. Integration

    • Develop APIs or plugins for popular industry software

  7. Monetization Strategy

    • Consider tiered pricing model based on usage or features
    • Offer freemium version to attract users

  8. Marketing

    • Focus on content marketing
    • Showcase accuracy and time-saving benefits

  9. Compliance

    • Ensure product complies with data protection and privacy regulations

For more information or to contribute to this project, please contact us or open an issue in this repository.