Automated 3D Model Metadata Generation Using Depth Camera Feature Vectors
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Solution Overview
Problem
Manually generating metadata for 3D models is labor-intensive, time-consuming, and often results in inconsistent and poorly organized data, especially when using CAD tools or depth cameras, leading to inaccuracies in tagging and classification.
Innovation Solution
A system and method for automatically generating metadata for 3D models using a scanner system with a depth camera, which supplies the media document to a feature extractor to generate a feature vector and classify it based on a set of classifications, then searches a database to identify similar media documents to merge metadata and populate the query model's metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual metadata generation is used for 3D models, then detailed and accurate metadata can be created, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of metadata generation with an automated computer-based system that uses depth camera scanning and machine learning algorithms to extract and generate metadata automatically, eliminating the need for manual labeling while maintaining high accuracy
Solution Approach 2:
The patent introduces depth cameras and feature extraction algorithms as intermediaries between the 3D model and the metadata, using these tools to automatically capture geometric features and generate descriptive tags without requiring direct human intervention in the metadata creation process
2Adaptability or versatility
If manual metadata generation is used for 3D models, then metadata can be customized, but the results become inconsistent and poorly organized
Solution Approach 1:
The patent changes the parameters of metadata generation from human-dependent variables to system-controlled variables, using standardized feature extraction algorithms and machine learning models that consistently apply the same criteria across all 3D models, thereby ensuring uniformity and organization in the generated metadata
Solution Approach 2:
The patent replaces the inconsistent human judgment process with a standardized automated system that uses depth camera data and machine learning algorithms to generate metadata according to consistent rules and criteria, eliminating variability in metadata quality and organization
3Productivity
If automated feature extraction is used for 3D models, then metadata generation speed increases, but the system complexity increases
Solution Approach 1:
The patent segments the automated metadata generation system into distinct modular components: depth camera scanning module, feature extraction module, machine learning classification module, and metadata generation module. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity
Data Source
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AI summary
According to one embodiment of the present invention, a method for automatically generating metadata for a media document includes: computing a feature vector of the media document using a convolutional neural network; searching a collection of media documents for one or more matching media documents having corresponding feature vectors similar to the feature vector of the media document, each media document of the collection of media documents being associated with metadata; generating metadata for the media document based on the metadata associated with the one or more matching media documents; and displaying the media document in association with the generated metadata.