AI 3D Property Layout Generation from Panoramic Room Classification
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Solution Overview
Problem
Existing methods for creating property layouts, such as floor plans, are labor-intensive and prone to errors due to manual measurements and lack of standardization, leading to inaccurate representations.
Innovation Solution
A system and method utilizing 3D data and neural networks to classify panoramic images, identify rooms and stories, and generate property layouts automatically, incorporating features like room dimensions and annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual measurement tools and software are used to create floor plans, then the process can be performed with basic equipment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical measurement tools (tape measures, laser distance measurers) with automated 3D scanning systems that capture spatial data electronically. This substitution eliminates the need for physical measurement and manual recording, directly reducing the time required while maintaining ease of use through automated data capture.
Solution Approach 2:
The system transforms the floor plan creation process from manual parameter input (dimensions, wall locations) to automated parameter extraction from 3D scan data. By changing the input method from human measurement to machine-based spatial analysis, the system dramatically reduces time investment while keeping the process accessible.
2Ease of manufacture
If manual measurement and drafting processes are used, then simple tools are sufficient, but accuracy is reduced due to human error
Solution Approach 1:
The patent replaces manual measurement processes with automated 3D scanning and neural network-based classification systems. This substitution eliminates human measurement errors while maintaining tool simplicity through integrated capture devices that combine scanning, processing, and generation capabilities in a single system.
Solution Approach 2:
The system creates accurate digital copies of physical spaces through 3D scanning, preserving exact dimensions and spatial relationships. These digital replicas serve as the basis for floor plan generation, ensuring measurement precision is maintained throughout the process while keeping the interface simple for users.
3Stability of the object's composition
If standardized floor plan creation processes are used, then consistency is improved, but the process becomes more complex and requires multiple verification steps
Solution Approach 1:
The system performs self-verification through automated neural network classification and consistency checking algorithms. The AI model independently validates the generated floor plan against the 3D scan data, eliminating the need for manual verification steps while maintaining high consistency standards. This self-service approach reduces process complexity without sacrificing quality control.
Solution Approach 2:
The system implements automated feedback loops where the neural network continuously refines floor plan generation based on classification accuracy and consistency metrics. This feedback mechanism ensures standardized output quality while automating the verification process, reducing the apparent complexity for end users.
4Measurement precision
If automated 3D data classification and neural networks are used, then accuracy and standardization are improved, but the system complexity increases
Solution Approach 1:
The patent implements a multi-functional system where a single integrated platform performs 3D data capture, neural network-based classification, room identification, and floor plan generation. This universal system consolidates multiple complex functions into one cohesive tool, improving accuracy through AI while managing overall system complexity through integration rather than separate components.
Solution Approach 2:
The system segments the complex floor plan creation process into distinct AI-powered modules: 3D data acquisition, panoramic image classification, room type identification, and layout generation. Each module handles a specific task with specialized algorithms, improving overall accuracy while organizing complexity into manageable, independent components that can be processed sequentially.
Data Source
AI summary
An example method includes receiving 3D data of an interior of a building that has one or more stories and one or more rooms on the one or more stories. The 3D data is classified by receiving multiple 360 degree panoramic images of the interior, applying a trained model to classify the multiple 360 degree panoramic images, and determining, based on applying the trained model to classify the multiple 360 degree panoramic images, one or more room classifications for the 3D data. One or more story identifications of the one or more stories and one or more room identifications of the one or more rooms are generated. A property layout of the building that includes the one or more story identifications and the one or more room classifications is generated. The property layout is provided for display.