3D Space Layout Recommendation Using Scanning and Neural Networks
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Solution Overview
Problem
Current methods for interior design and layout recommendations in 3D spaces rely heavily on human expertise, leading to potential errors and time-consuming decision-making processes.
Innovation Solution
A computing apparatus and method that scans a 3D space to identify attribute information such as appearance measurements, space type, and furniture style, and provides automatic recommendations for style and furniture arrangements using machine learning algorithms and neural networks to reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human expertise is used for interior design and layout recommendations, then the quality of recommendations may be maintained, but the decision-making time increases and human errors occur
Solution Approach 1:
The patent replaces the mechanical system of human expert judgment with an automated computer vision system. The system uses scanning devices to capture spatial data, neural networks to analyze attribute information, and recommendation algorithms to generate layout suggestions, eliminating the need for human experts to manually measure and design while reducing decision-making time.
Solution Approach 2:
The system enables self-service by automatically analyzing the 3D space attributes and generating recommendations without requiring professional interior designers. The automated pipeline includes space scanning, attribute extraction, and intelligent recommendation generation that users can access independently, reducing both time loss and human error.
2Reliability
If human expertise is used for interior design and layout recommendations, then the quality of recommendations may be maintained, but human errors occur
Solution Approach 1:
The patent replaces the mechanical system of human expert judgment with an automated computer vision system. The system uses scanning devices to capture spatial data, neural networks to analyze attribute information, and recommendation algorithms to generate layout suggestions, eliminating the need for human experts to manually measure and design while reducing decision-making time.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated recommendations are validated against the extracted attribute information. The neural networks continuously learn from the relationship between space attributes and optimal layouts, providing feedback that improves recommendation accuracy and reduces human errors over time.
3Loss of time
If automatic scanning and analysis is used, then decision-making time is reduced, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the complex system into distinct functional modules: scanning module for capturing spatial data, attribute extraction module for identifying space characteristics, recommendation module for generating layouts, and validation module for checking accuracy. This modular architecture reduces overall system complexity while maintaining fast decision-making capabilities.
Solution Approach 2:
The system achieves universality by using a single integrated platform that performs multiple functions: space scanning, attribute analysis, furniture recommendation, and layout optimization. This multi-functional approach consolidates what would otherwise require multiple separate tools and expert processes into one unified system, reducing complexity while improving efficiency.
Data Source
AI summary
An arrangement recommendation method of a three-dimensional (3D) space and a computing apparatus are provided. In the method, a 3D space is obtained, and the 3D space is established by scanning a space. Attribute information of the 3D space is identified, and the attribute information includes appearance measurement, space type, furniture type, and/or furniture style. Recommendation information of the 3D space is provided according to the attribute information, and the recommendation information includes a style recommendation and/or a furniture recommendation. Accordingly, the decision-making time for recommendation may be reduced.