Mixed-Reality Object Recognition With Spatial Anchoring
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Solution Overview
Problem
Conventional VR and AR applications lack seamless integration of ultra-high resolution holographic objects with real physical objects, face high computational requirements, suffer from latency and adaptability issues, and lack automated integration and context-aware interactions, limiting their use in mixed-reality environments.
Innovation Solution
A real-time object recognition and integration system using a diverse set of sensors, a multi-modal sensing module, neural networks for object detection, spatial mapping, and a visual programming interface connector, along with safety protocols, to enable seamless interaction between virtual and real elements in mixed-reality environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based object recognition is used, then object recognition accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The patent introduces an intermediary computing system that acts as a mediator between the MR device and the object recognition task. The MR device captures images and transmits them to a separate computing system that performs the computationally intensive deep learning-based object recognition, then returns results to the MR device. This intermediary approach allows high accuracy recognition without overloading the MR device's computational resources.
2Speed
If real-time visual processing is implemented, then interaction responsiveness is improved, but computational intensity increases
Solution Approach 1:
The patent segments the visual processing pipeline into distinct stages: image capture by MR device, transmission to computing system, object recognition processing, and result return. This segmentation allows computationally intensive tasks to be performed separately while maintaining real-time interaction responsiveness through optimized data transmission and parallel processing capabilities.
3Loss of information
If conventional AR applications are used, then information augmentation is provided, but seamless integration with real physical objects is lost
Solution Approach 1:
The patent merges the strengths of both VR and AR by creating a mixed-reality system that combines virtual holographic objects with real physical objects in the same spatial environment. The system uses object recognition to identify real objects, then integrates virtual elements with them, allowing information augmentation while maintaining seamless integration with the physical world.
4Productivity
If automated object integration is implemented, then programming efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically performs object recognition, spatial mapping, and integration tasks without requiring manual programming. The automated pipeline detects objects in the environment, creates corresponding virtual representations, and integrates them into the MR experience automatically, significantly improving programming efficiency despite the underlying system complexity.
Data Source
AI summary
A novel real-time object recognition and integration system for mixed-reality (MR) programming and a related method of operating the system enable robust, adaptable, and seamless context-aware interactions between physical objects and digital representations in an MR environment. The novel system incorporates a multi-modal sensing module connected to a diverse set of sensors (e.g., cameras, depth sensors, IMUs) to gather rich environmental data and a real-time object detection and recognition engine that employs a uniquely-efficient neural network architecture for rapid object detection and classification. The novel system also incorporates a spatial mapping and anchoring unit to create an accurate 3D map of the environment and determine precise object positions and orientations. Furthermore, the novel system may also include a dynamic node generation module, a context analysis engine, an adaptive learning module, a visual programming interface connector, an interaction modeling system, a performance optimization module, and a safety and validation layer.