Machine Learning 3D Model Generation for Virtual Building Experiences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating 3D virtual experiences from 2D input data are limited by quality of renders, require manual effort, are time-consuming and error-prone, and are dependent on specialized hardware, and are inefficient on devices with limited resources.
Innovation Solution
A method and system that uses machine learning to analyze user profile data to generate interactive 3D models, transmit them to user devices, and update based on user reactions, eliminating the need for specialized hardware and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to generate 3D virtual experiences from 2D input data, then quality of renders can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical processes with automated machine learning models and algorithms. The system uses ML models to automatically generate 3D virtual experiences from 2D input data, eliminating manual intervention while maintaining high quality renders through sophisticated automated processing.
Solution Approach 2:
The system changes the processing parameters by using advanced machine learning models that can process data more efficiently. The ML models optimize rendering parameters automatically, achieving high-quality outputs faster than manual methods by dynamically adjusting processing parameters based on input characteristics.
2Manufacturing precision
If specialized hardware like 3D depth cameras are used to create virtual experiences, then quality can be improved, but device complexity and cost increase
Solution Approach 1:
The system creates virtual copies of 3D environments from 2D input data using machine learning. Instead of requiring specialized hardware to capture real-world 3D data, the ML models generate accurate 3D representations by learning from 2D images, effectively copying spatial relationships without physical depth sensors.
Solution Approach 2:
The patent replaces expensive, complex specialized hardware with software-based solutions that run on standard devices. The machine learning models can be deployed on conventional smartphones and computers, eliminating the need for costly 3D depth cameras while maintaining acceptable quality through algorithmic processing.
3Adaptability or versatility
If 3D virtual experiences are transmitted to user devices with limited resources, then accessibility is improved, but transmission time and processing time increase
Solution Approach 1:
The system segments the 3D virtual experience into optimized components suitable for transmission to devices with limited resources. The machine learning models generate compressed, adaptive 3D data structures that can be efficiently transmitted and rendered on mobile devices without requiring full high-fidelity 3D assets.
Solution Approach 2:
The system dynamically changes rendering and transmission parameters based on the capabilities of the target device. The ML models adjust quality levels, resolution, and data compression ratios according to device resources, enabling fast transmission and processing on mobile devices while maintaining acceptable visual quality.
4Productivity
If automated machine learning methods are used to generate 3D virtual experiences, then productivity and speed are improved, but system complexity increases
Solution Approach 1:
The patent employs universal machine learning models that can handle multiple types of 2D input data and generate various 3D virtual experience formats. These multi-functional models reduce overall system complexity by consolidating multiple specialized processing functions into single adaptable algorithms that work across different scenarios.
Data Source
AI summary
Disclosed herein is a method of provisioning a virtual experience of a building based on user preference. The method may include receiving an identity data associated with an identity of a user, retrieving a user profile data based on the identity data, analyzing the user profile data using a machine learning model, determining at least one preference data based on the analyzing, identifying at least one virtual utility object based on the at least one preference data, generating an interactive 3D model data comprising the at least one virtual utility object, transmitting the interactive 3D model data to a user device configured to present the interactive 3D model data, receiving a reaction data from the user device. Further, the user device may include at least one sensor configured to generate the reaction data based on a behavioral reaction of a user consuming the interactive 3D model data.