3D Real-World Object Modeling for Cross-Environment AR
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing augmented reality (AR) technologies do not support the dynamic and interactive user experience of viewing a physical object in a different real-world setting, such as previewing a product in a user's living room, while allowing physical interaction with the product.
Innovation Solution
A system and method for generating 3D AR scenes by processing 3D representations of real-world environments, using 3D scan data to detect and position physical objects of interest, and combining them with live images based on defined triggers and rules, such as pose detection, user input, or proximity, to create an AR version of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual objects are overlaid on real-world scenes using existing AR technologies, then virtual information can be displayed in the real-world environment, but dynamic and interactive user experience of viewing physical objects in different real-world settings is not supported
Solution Approach 1:
The system creates virtual copies of physical objects by generating 3D representations from images. These digital twins can be placed in different virtual environments while maintaining the appearance and properties of the original physical objects, enabling users to view products in various settings without physically moving them.
Solution Approach 2:
The system transitions from 2D images to 3D virtual representations, adding a dimensional layer that enables spatial manipulation and placement in different environments. This dimensional transformation allows objects to be viewed from multiple angles and positioned in various real-world settings through AR.
2Measurement precision
If 3D representations are generated from images to enable object positioning, then accurate positioning and texturing can be achieved, but the system complexity increases
Solution Approach 1:
The system replaces complex mechanical measurement and positioning systems with image-based computational methods. By using computer vision algorithms to extract 3D information from 2D images, the system achieves accurate positioning without requiring sophisticated hardware measurement devices.
Solution Approach 2:
The system enables objects to self-position and self-texture by automatically generating 3D representations from images. The computational process autonomously extracts geometric and textural information, eliminating the need for manual positioning and texturing operations.
Data Source
AI summary
A computer-implemented is disclosed. The method includes: obtaining a three-dimensional (3D) representation of a first real-world environment; identifying a real-world object of interest in a second real-world environment, the first real-world environment different from the second real-world environment; determining a first position in the 3D representation of the first real-world environment corresponding to the real-world object of interest; and generating an augmented reality (AR) version of the first real-world environment for presentation in the second real-world environment using the 3D representation of the first real-world environment and based on positioning the real-world object of interest in the first position in the AR version of the first real-world environment.