Crowd-Sourced 3D Model Update via Image Recognition
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Solution Overview
Problem
Current 3D modeling systems for augmented reality struggle to accurately and efficiently update 3D models of real-world environments due to the need for extensive reference data and lack of real-time adaptation to changes in the environment.
Innovation Solution
A crowd-sourced modeling system that collects and updates 3D models by receiving image data from multiple devices, identifying changes in the environment, and incentivizing users to capture and submit images of changes, thereby generating up-to-date 3D models using image recognition and geo-location techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D modeling systems use extensive reference data to generate accurate 3D models, then the accuracy of the 3D model is improved, but the time required to update the model and the complexity of the system increases
Solution Approach 1:
The system enables users to automatically update 3D models by capturing images of environmental changes using their personal devices. The crowd-sourced approach allows the environment to self-update its digital twin without requiring professional surveyors or manual intervention, thus reducing update time while maintaining accuracy through user-contributed data
Solution Approach 2:
The system implements continuous feedback loops where users receive notifications when changes are detected in their environment, capture updated images, and submit them to the system. This feedback mechanism ensures 3D models are continuously updated with current environmental data, balancing accuracy requirements with timely updates
2Measurement precision
If traditional systems require professional surveyors and manual data collection for 3D modeling, then the initial model accuracy is improved, but the cost and complexity of maintaining updated models increases
Solution Approach 1:
The system creates digital copies of the physical environment through 3D modeling, allowing virtual updates to reflect real-world changes. Instead of requiring physical surveyors to manually measure and update every change, the system uses image processing to copy and integrate new environmental data into the existing 3D model, simplifying the update process while maintaining accuracy
Solution Approach 2:
The system replaces manual mechanical surveying processes with automated image processing and computer vision algorithms. Users simply capture images with their smartphones, and the system automatically processes these images to update the 3D model, eliminating the need for complex surveying equipment and trained personnel while maintaining model accuracy
3Adaptability or versatility
If 3D models are updated frequently to reflect real-time environmental changes, then the realism of AR content is improved, but the quantity of reference data required increases
Solution Approach 1:
The system extracts only the essential change information from the environmental updates rather than requiring complete re-surveying of the entire environment. By identifying and processing only the specific changes that occur (such as new buildings, removed objects, or modified structures), the system maintains real-time adaptability while minimizing the quantity of reference data that needs to be processed and stored
Data Source
AI summary
A crowd-sourced modeling system to perform operations that include: receiving image data that comprises image attributes; accessing a 3D model based on at least the image attributes of the image data, wherein the 3D model comprises a plurality of parts that collectively depict an object or environment; identifying a change in the object or environment based on a comparison of the image data with the plurality of parts of the 3D model, the change corresponding to a part of the 3D model from among the plurality of parts; and generating an update to the part of the 3D model based on the image attributes of the image data.