Virtual Mesh Refinement via Physical Contact Prioritization
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Solution Overview
Problem
The refinement of virtual mesh models and reconstruction of physical environments consume significant time and computing resources, often prioritizing irrelevant features and resulting in less accurate models for darkly colored or highly featured surfaces without human intervention.
Innovation Solution
A mobile device with touch sensors and positioning sensors, worn as a glove, allows users to physically interact with objects to define priority locations for mesh refinement, enhancing the accuracy of virtual mesh models by capturing touch and location data to infer object hardness and prioritize refinement in relevant areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic mesh refinement is performed without human intervention, then processing speed is improved, but manufacturing precision deteriorates for darkly colored or highly featured surfaces
Solution Approach 1:
The system performs preliminary actions by having users physically touch and mark locations of interest on the physical environment before mesh refinement begins. These pre-marked locations are stored and used to guide subsequent automated refinement processes, ensuring that critical areas are prioritized without requiring continuous manual intervention during the actual refinement process.
Solution Approach 2:
The system incorporates feedback mechanisms where users can physically interact with the environment using a mobile device with touch sensors. The system detects these physical contacts, processes the feedback information, and uses it to adjust and improve the mesh refinement process, creating a closed-loop system that combines automatic processing with human-guided precision.
2Manufacturing precision
If comprehensive mesh refinement is performed on all features, then manufacturing precision is improved, but loss of time increases due to processing irrelevant features
Solution Approach 1:
The system applies local quality by concentrating mesh refinement efforts specifically on locations that users have physically marked as important, rather than uniformly refining the entire environment. This localized approach ensures high precision where needed while avoiding time-consuming processing of irrelevant areas, creating a non-uniform refinement strategy that optimizes both accuracy and efficiency.
Solution Approach 2:
The refinement process is segmented into different priority levels based on user input. The system divides the physical environment into multiple regions with different refinement priorities, processing high-priority areas (marked by user contact) with higher detail while using coarser representation for low-priority areas, thereby reducing overall processing time while maintaining necessary precision.
3Device complexity
If automated imaging is used for surface reconstruction, then device complexity is reduced, but measurement precision deteriorates for certain surface types
Solution Approach 1:
The system introduces an intermediary element in the form of a mobile device with touch sensors that users physically interact with. This intermediary bridges the gap between simple automated imaging and complex manual measurement techniques, allowing users to provide tactile feedback that enhances measurement precision for challenging surfaces while maintaining relative system simplicity through automated processing of the collected data.
Data Source
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AI summary
Examples are disclosed that relate to refining virtual mesh models through physical contacts. For example, a hand-mounted mobile device, such as a wearable glove, may be used to create and/or emphasize specific points within a virtual mesh model of a physical environment. An indication of physical contact of an interface of the mobile device with a physical object may be obtained via a touch sensor of the mobile device. A location and/or an orientation of the interface of the mobile device during the physical contact with the physical object may be identified based on sensor data obtained from one or more positioning sensors. Location data indicating the location may be stored in a data storage device from which the location data may be referenced. In an example, refinement of a virtual mesh model of a physical environment containing the physical object may be prioritized based on the location data.