3D AR Item Placement Using Depth-Based Video Scene Modeling
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Solution Overview
Problem
Existing AR systems require users to manually select and position AR elements, which is time-consuming and often results in unrealistic placements, leading to user frustration and resource wastage.
Innovation Solution
The system automatically determines the 3D placement and orientation of AR items based on a real-world environment's 3D model, using depth information and machine learning techniques, and displays a marker indicating the recommended placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually select and position AR elements, then placement precision can be controlled, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system automatically performs environment scanning, 3D modeling, and AR element placement without requiring manual user intervention. The AR system self-determines placement positions based on depth information and automatic environment analysis, eliminating the time-consuming manual selection and positioning process while maintaining accurate placement.
2Measurement precision
If users manually position AR elements, then placement accuracy can be achieved, but ease of operation deteriorates due to complex interface navigation
Solution Approach 1:
The system automatically scans the environment using depth sensors, generates 3D models, and determines optimal AR element placement positions without requiring users to navigate complex interfaces. The system self-performs all positioning operations based on automatic environment analysis.
Solution Approach 2:
The patent replaces manual mechanical interaction (users physically moving sliders, rotating controls, and clicking interface elements) with automated computational processes. Depth information from sensors is processed algorithmically to automatically determine placement positions, substituting mechanical user operations with computational automation.
3Ease of operation
If automatic AR placement is implemented, then user effort is reduced, but placement realism may deteriorate without proper depth perception
Solution Approach 1:
The system replaces manual depth perception and judgment with automated depth sensors and 3D modeling algorithms. These sensors objectively measure environmental geometry and generate accurate 3D models, providing reliable depth information for realistic AR element placement without requiring user interpretation.
Solution Approach 2:
The patent introduces depth sensors and 3D models as intermediary components between the physical environment and AR element placement. These intermediaries objectively capture and represent spatial relationships, serving as a reliable bridge that translates real-world geometry into accurate virtual element positioning.
4Adaptability or versatility
If manual AR element selection is required, then user control over placement is maintained, but resource consumption increases due to repeated trial and error
Solution Approach 1:
The system performs preliminary environment scanning and 3D model generation before AR element placement. By pre-processing the environment data and pre-determining optimal placement positions, the system eliminates the need for repeated trial and error operations, reducing computational resource consumption while maintaining placement accuracy.
Solution Approach 2:
The system uses depth information and 3D models as feedback mechanisms to automatically determine correct placement positions. This feedback loop allows the system to objectively assess environmental constraints and adjust placement accordingly, eliminating wasteful trial and error cycles that consume computational resources.
Data Source
AI summary
Aspects of the present disclosure involve a system for presenting AR items. The system performs operations including receiving a video that includes a depiction of one or more real-world objects in a real-world environment and obtaining depth information related to the real-world environment; and generating a 3D model of the real-world environment. The operations further include determining 3D placement and orientation for an AR item based on data associated with the AR item and the 3D model of the real-world environment and causing display of a marker in the video that specifies the 3D placement and orientation of the AR item. The operations further include rendering a display of the AR item within the video according to the 3D placement and orientation in response to movement of the marker within the video.