AR Image Generation Using Reference Point Mapping
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Solution Overview
Problem
Conventional augmented reality (AR) and virtual reality (VR) systems fail to accurately identify the real-world locations of captured objects, leading to inappropriate placement of virtual objects within the real-world environment, which reduces the realism of the AR or VR scene.
Innovation Solution
An augmented reality image generation system that uses a computing platform with a hardware processor and system memory to store software code and virtual object libraries, which captures camera images and sensor data to create a spatial map, identifies reference points with predetermined real-world locations, and maps real-world objects to their correct locations, allowing for accurate placement of virtual objects within the AR or VR scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional AR/VR systems are used to merge virtual objects with real-world features, then the system complexity is reduced and ease of operation is improved, but the measurement precision of real-world object locations deteriorates, leading to inappropriate placement of virtual objects
Solution Approach 1:
The patent introduces an intermediary computing platform that acts as a mediator between the camera system and the AR/VR display. This platform receives camera images, identifies real-world objects and their locations, determines appropriate placement positions for virtual objects, and generates the merged AR/VR images. By inserting this intermediary layer, the system achieves high measurement precision for object locations without requiring the entire system to become more complex, as the location identification and virtual object placement logic is centralized in the computing platform.
Solution Approach 2:
The computing platform performs self-service by automatically identifying real-world objects in camera images, determining their locations, and calculating appropriate placement positions for virtual objects without requiring manual intervention. The system uses image processing algorithms to autonomously analyze the camera feed, identify objects of interest, and compute where virtual objects should be positioned to achieve realistic integration with the real-world environment.
2Reliability
If accurate identification of real-world object locations is implemented, then the realism of AR/VR scenes is improved, but the loss of time for processing and generating augmented reality images increases
Solution Approach 1:
The computing platform performs preliminary actions by pre-processing camera images to identify real-world objects and their locations before virtual objects need to be placed. The system continuously analyzes the camera feed, maintains an updated understanding of the real-world environment, and pre-determines placement positions for virtual objects. This preliminary processing allows the system to quickly generate AR/VR images with accurate object placement without experiencing time delays during the actual augmentation process.
Solution Approach 2:
The system maintains continuous processing of camera images to track real-world objects and their locations over time. Rather than performing discrete, time-consuming analysis for each frame, the computing platform continuously processes the video feed, maintaining an ongoing understanding of the environment. This continuous action allows the system to generate realistic AR/VR scenes in real-time by building upon previously processed information rather than starting from scratch for each image.
3Measurement precision
If virtual objects are accurately positioned based on real-world object locations, then the apparent realism of the AR scene is improved, but the difficulty of detecting and measuring object positions increases
Solution Approach 1:
The patent replaces manual or mechanical methods of object detection with automated image processing and computer vision algorithms. The computing platform uses software-based object recognition to identify real-world objects in camera images and automatically determine their positions. This substitution of mechanical detection methods with computational algorithms reduces the difficulty of detecting and measuring object positions while maintaining high measurement precision for virtual object placement.
Solution Approach 2:
The system creates a digital copy or representation of the real-world environment by processing camera images and generating a computational model of object locations. The computing platform analyzes the visual data to produce an accurate digital replica of the spatial arrangement of real-world objects, which then serves as the basis for positioning virtual objects. This copying approach simplifies the detection and measurement process by working with digital image data rather than directly measuring physical object positions.
Data Source
AI summary
According to one implementation, an augmented reality image generation system includes a display, and a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a camera image depicting one or more real-world object(s), and to identify one or more reference point(s) corresponding to the camera image, each of the reference point(s) having a predetermined real-world location. The software code further maps the real-world object(s) to their respective real-world location(s) based on the predetermined real-world location(s) of the reference point(s), merges the camera image with a virtual object to generate an augmented reality image including the real-world object(s) and the virtual object, and renders the augmented reality image on the display. The location of the virtual object in the augmented reality image is determined based on the real-world location(s) of the real-world object(s).