Machine Learning Virtual Environment Generation
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Solution Overview
Problem
The existing methods for creating virtual reality and augmented reality environments are time-consuming and laborious, requiring manual creation of geometry and texture data, which can lead to inconsistent results and slow performance, making it difficult for inexperienced developers to achieve immersive and smooth experiences.
Innovation Solution
A system and method that utilize a machine learning algorithm and pre-built 3D components to automatically generate virtual environments, including a scene recognizer, parser, asset mapping module, and display module, allowing users to upload or select scenes, analyze intents and entities, and transform geometry data to create a contiguous 3D virtual environment with contextual suggestions and themeable components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation of geometry and texture data is used, then quality and consistency of virtual environment can be controlled, but time consumption increases significantly
Solution Approach 1:
The system uses pre-built 3D components and pre-trained machine learning models to generate virtual environments automatically. The geometry data and texture data are prepared in advance as reusable assets that can be quickly assembled and transformed to create consistent virtual environments without manual creation, thus reducing time consumption while maintaining quality through standardized pre-validated components
Solution Approach 2:
The system copies and transforms existing 3D components and assets to create virtual environments. Instead of manually creating geometry and textures from scratch, the system uses machine learning to generate transformations and variations of pre-existing high-quality assets, maintaining consistency while dramatically reducing creation time
2Ease of manufacture
If manual creation of virtual environments is used, then detailed control over geometry and texture is possible, but developer skill requirement increases
Solution Approach 1:
The system performs self-service by automatically generating virtual environments using machine learning algorithms. The machine learning model autonomously processes input parameters, selects appropriate 3D components, generates geometry and texture data, and assembles the virtual environment without requiring manual intervention or specialized skills, thus making the process accessible to developers with minimal technical expertise while maintaining detailed control through parameter-based customization
3Productivity
If pre-built 3D components are used, then generation speed increases, but customization flexibility may be reduced
Solution Approach 1:
The system applies dynamics by using machine learning to generate dynamic transformations and variations of pre-built 3D components. The machine learning model can adapt the same base components in multiple ways based on input parameters, creating diverse customized versions while maintaining the efficiency of using pre-built assets. This allows rapid generation speed to be combined with high customization flexibility through intelligent variation rather than static copying
Data Source
AI summary
The embodiments herein provide a system and method for generating data for a three-dimensional (3D) environment using existing information from a virtual or augmented reality scene is disclosed. The method includes reading data from a data store. The data comprising component identifying data and component position data for at least one of said 3D components. Further, the component data is analyzed for at least one identified component from a data store, the component data including at least 3D geometry data for the component. Thereafter, at least one component of the 3D geometry data is transformed using component position data to provide 3D virtual environment data for a specific 3D virtual environment. The 3D virtual environment data defines a substantially contiguous 3D surface enclosing the 3D virtual environment. The system includes a scene recognizer, parser, asset mapper, machine learning driven design algorithm, and asset store reorganizer.