Reality Capture Metadata Classification for Asset Generation
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Solution Overview
Problem
Manually classifying source files of reality capture data into asset classes is inefficient, impractical, and error-prone, leading to degraded processing of digital reality capture assets and wastage of computational and network resources.
Innovation Solution
A method and system for automatically classifying source files of reality capture data into asset classes using metadata, utilizing a classification model to process payloads generated from the source files, thereby reducing the amount of data transmitted and processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of source files is performed, then classification accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system enables automatic self-classification of source files by extracting and analyzing metadata independently, eliminating the need for manual intervention while maintaining classification accuracy through automated algorithms that process file characteristics and generate appropriate asset class assignments
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational systems that extract metadata, analyze file characteristics, and classify source files using algorithms, thereby reducing time consumption while maintaining or improving classification accuracy
2Loss of information
If all source files are transmitted for processing, then complete data is available for analysis, but network and computational resources are wasted
Solution Approach 1:
The system extracts only the necessary metadata and key characteristics from source files for classification purposes, rather than transmitting and processing the complete file data. This extraction approach maintains essential information for accurate classification while significantly reducing network and computational resource requirements
Solution Approach 2:
The patent segments the data processing task by separating essential metadata extraction from complete file processing. Only the segmented metadata portions are transmitted and analyzed for classification, while the full files are processed only after classification is determined, optimizing resource utilization
3Productivity
If incorrect classification occurs, then processing may continue, but quality of digital reality capture assets deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that validate classification results and enable correction of misclassifications. By reviewing and verifying asset class assignments, the system ensures that only correctly classified source files proceed to digital asset generation, maintaining high output quality while preserving processing efficiency
Data Source
AI summary
A method for classifying a set of source files of reality capture data into an asset class of digital reality capture assets may include receiving, from a user device, a set of payloads including metadata of the set of source files of the reality capture data corresponding to a region of interest captured by a camera. The method may include classifying the set of source files of the reality capture data into the asset class of the digital reality capture assets, based on the set of payloads including the metadata of the set of source files of the reality capture data. The method may include providing, to the user device, information identifying the asset class of the digital reality capture assets to which the set of source files are classified to permit a digital reality capture asset, corresponding to the asset class, of the region of interest to be generated based on the set of source files.