Smart House Planner: An AI-Powered System for Interactive Home Design, 3D Modelling, and Cost Estimation

Abstract

The Smart House Planner is an AI-powered solution designed to simplify the process of residential home design by combining intelligent planning, 3D modelling, and real-time cost estimation through an interactive conversational interface. With the rising demand for personalized housing solutions and the complexity of balancing aesthetics, functionality, and budget, there is a growing need for systems that can automate architectural planning while remaining user-friendly. This project addresses that challenge by integrating Gemini AI for intelligent design recommendations, Blender for automated 3D visualization, and a cost estimation engine for transparent financial planning. The system enables users to specify requirements such as housing type (1BHK, 2BHK, 3BHK, Duplex, Villa), dimensions, and style preferences via a modern Django-based web interface. Gemini AI processes these inputs to generate optimized layouts by considering space utilization, and functional flow. These layouts are then transformed into realistic, interactive 3D models using Blender, allowing users to explore their prospective homes virtually. Simultaneously, the cost estimation module calculates material, labour, costs, producing downloadable reports that support informed decision-making and budget control. By unifying AI-driven design intelligence, real-time 3D modelling, and detailed cost breakdowns, Smart House Planner provides a comprehensive platform that enhances user engagement, reduces reliance on professional intervention, and democratizes access to architectural planning. This solution demonstrates how AI, visualization technologies, and web-based interfaces can transform the way individuals approach housing design, making the process more efficient, transparent, and accessible to both professionals and non-experts.

Introduction

The Smart House Planner is an AI-powered, interactive platform designed to simplify and democratize residential home design and planning. It addresses the limitations of traditional architecture processes—such as heavy dependence on professionals, complex tools, and disjointed cost estimation—by offering an integrated, user-friendly solution that combines AI-generated design, 3D visualization, and cost estimation.

Key Features and Modules:

1. AI-Powered Design (Gemini AI):

   • Uses natural language processing to interpret user requirements (house type, budget, preferences).

   • Generates optimized, personalized floor plans.

   • Supports interactive, real-time refinement through chat-based inputs.

2. 3D Modeling and Visualization (Blender):

   • Converts AI-generated layouts into realistic, interactive 3D models.

   • Allows users to virtually explore their home’s design and spatial layout.

3. Cost Estimation:

   • Offers transparent, real-time breakdowns of construction costs (materials, labor, etc.).

   • Helps users budget accurately and make informed financial decisions.

4. Web-Based Interface (Django):

   • Centralized platform for input, visualization, and documentation.

   • Built with HTML, CSS, JavaScript, and Tailwind for a modern UI/UX.

5. Conversational Interaction:

   • Chat-based assistant guides users through design decisions, instantly adapts to new inputs.

6. Reporting and Documentation:

   • Generates downloadable reports (PDF/Excel) with layouts, visuals, and budget summaries.

Problem with Existing Systems:

• Heavy reliance on architects and professional tools (e.g., AutoCAD, Revit).

• Fragmented processes across design, visualization, and cost estimation.

• Steep learning curve for non-professionals.

• Limited customization and lack of AI or conversational tools.

Objectives:

• Provide AI-driven, accessible home design for non-experts.

• Enable real-time 3D visualization and budgeting.

• Simplify and centralize the home planning process.

• Promote user independence and creativity in design.

Software & Technology Stack:

• Backend: Python, Django, FastAPI (for async API communication).

• Frontend: HTML, CSS, JavaScript, Tailwind CSS.

• AI Module: Gemini AI (for layout generation & interaction).

• 3D Engine: Blender with Python scripting (for model generation).

• Database: SQLite (lightweight storage for user data, models, and cost breakdowns).

System Testing & Validation:

• Unit Testing: Tests individual modules (AI, 3D modeling, cost engine).

• Integration Testing: Ensures smooth data flow between components.

• Performance Testing: Simulates load to check real-time responsiveness.

• End-to-End Testing: Validates full user journey from input to report.

• Expert Review: Architects verify functionality and practicality of layouts.

• Cost Accuracy: Compared against real-world data for validation.

Advantages:

• Reduces dependency on architects and manual planning.

• Offers immersive and dynamic user experiences.

• Encourages budget-aware decision-making.

• Helps real estate firms and educational institutions streamline planning and teaching.

• Promotes collaborative and iterative design through AI chat interface.

• Aligns with modern goals of affordability, sustainability, and smart housing.

Scope for Broader Impact:

• Suitable for homeowners, students, architects-in-training, and small firms.

• Can be integrated into academic, construction, and interior design workflows.

• Empowers users with control, creativity, and clarity in home planning.

Conclusion

The Smart Home Planner project demonstrates how the integration of artificial intelligence, 3D modelling, and web-based technologies can transform traditional house planning into a modern, efficient, and user-friendly process. Unlike conventional methods that rely on lengthy consultations and approximate estimates, this system provides a transparent, interactive platform that empowers homeowners to design their dream houses with confidence. By leveraging Gemini AI, the system intelligently interprets user inputs, generates optimized layouts, and offers personalized design recommendations. The integration of Blender enables the creation of realistic 3D visualizations, helping users clearly understand space utilization and aesthetics before construction begins. Meanwhile, the cost estimation module ensures financial clarity through detailed and accurate breakdowns of expenses. The Django-based interface further enhances accessibility, offering a seamless, intuitive platform where users can input requirements, interact with the AI, and download comprehensive reports. Overall, the Smart Home Planner bridges the gap between imagination and execution by combining automation, creativity, and financial precision. It empowers individuals to make informed decisions, reduces the time and cost associated with traditional planning, and ensures that homeowners have a clear vision of their future homes. This project stands as a step toward modernizing the architecture and construction industry, making house planning smarter, more reliable, and more accessible to everyone.

References

[1] A Review of Artificial Intelligence in Enhancing Architectural Design — This paper examines how AI technologies are being applied within architectural design to improve design efficiency, data analysis, and sustainability. [2] Generative AI in Architectural Design: Application, Data, and Practice — A systematic review of generative AI tools (2014-2024) and their roles in architecture, highlighting use-cases and challenges. [3] Cost Estimation Model Based on Building Information Modelling and Its Applications — This study describes how BIM can be used to build accurate cost estimation systems during the design phase. [4] 5D BIM for Cost Estimation, Cost Control, and Design Change Management — This paper explores how 5D BIM links 3D models with cost and scheduling data, enabling dynamic cost control through design changes. [5] Building Information Model for Cost Estimation and Scheduling in Building Architecture — Focuses on combining BIM models with scheduling tools to estimate cost and project timelines. [6] Generative AI Models for Different Steps in Architectural Design — Discusses various generative AI techniques and their applicability across different stages of architecture. [7] The Role of Artificial Intelligence (AI) in Architectural Workflow — Reviews how AI enhances creativity, optimization, and automation within architectural design practices. [8] Visualization Analysis of Urban Planning Assistant Decision Networks — Examines 3D systems and intelligent computing to assist with urban planning decisions and spatial visualization. [9] Integrating Computer Vision in Construction Estimations and 3D Modellings — Proposes methods to generate 3D models and material quantity estimates from 2D scanned plans using computer vision. [10] Interactive Design by Integrating Large Pre-Trained Language Models and BIM — Explores combining pre-trained LLMs with BIM tools for dynamic architectural design and intent understanding.

Copyright

Copyright © 2025 Dr. K. Jagadeesh, Karthik A, Mukilesh Kumar S, Nivash S, Nithyasri R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.