3D Asset Quality Control Using Color and Texture Scoring
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Solution Overview
Problem
Manual quality control checks for 3-dimensional (3D) assets are inconsistent due to subjective bias, time-consuming, and inefficient for large-scale applications.
Innovation Solution
An automated quality control system using artificial intelligence and machine learning to assess 3D assets by comparing them to reference images, evaluating color and texture similarity, and generating quality scores based on predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality control checks are used to review 3D assets, then subjective bias and inconsistency are introduced, but automation and efficiency are reduced
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that uses machine learning models and algorithms to objectively evaluate 3D assets. This substitution eliminates human subjective bias while maintaining high processing speeds through automated computation, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system enables self-service quality control by allowing the automated platform to independently assess 3D assets without requiring human reviewer intervention for each item. The machine learning models automatically perform the evaluation, comparison, and scoring functions, achieving both consistency and efficiency simultaneously.
2Measurement precision
If manual quality control checks are used to compare 3D assets with reference images, then detailed quality assessment is possible, but time consumption increases
Solution Approach 1:
The automated system performs continuous quality assessment operations without interruption, processing multiple 3D assets in sequence without the breaks and delays inherent in manual review. The system maintains continuous computational analysis of color, texture, and geometric properties, achieving both precision and speed through uninterrupted automated processing.
Solution Approach 2:
The patent substitutes manual visual comparison with automated computer-based image processing and machine learning algorithms that continuously analyze 3D assets against reference images. This substitution enables precise measurement of quality attributes while dramatically reducing the time required for each assessment.
3Adaptability or versatility
If manual quality control checks are performed by multiple reviewers, then diverse perspectives are obtained, but inconsistency due to subjective bias increases
Solution Approach 1:
The system changes the fundamental parameter of assessment from human subjective judgment to objective computational metrics. By transforming the evaluation criteria into quantifiable parameters such as color distance, texture similarity scores, and geometric deviation measurements, the system achieves both versatility in assessment dimensions and consistency in results without relying on human reviewers.
Solution Approach 2:
The patent replaces the mechanical process of multiple human reviewers with an automated system that applies consistent algorithms to all assets. This substitution eliminates the inconsistency introduced by subjective bias while maintaining the ability to evaluate multiple aspects of quality through different computational metrics.
4Manufacturing precision
If manual quality control processes are used for large-scale 3D asset processing, then detailed review is possible, but efficiency and scalability are reduced
Solution Approach 1:
The patent segments the quality control process into distinct computational modules that evaluate different aspects of 3D assets independently (color accuracy, texture quality, geometric fidelity). This segmentation allows the system to maintain thoroughness in each assessment dimension while processing multiple assets simultaneously through parallel computation, achieving both precision and high productivity at scale.
Solution Approach 2:
The system substitutes manual detailed review with automated computational analysis that can simultaneously evaluate multiple quality attributes across large numbers of assets. This substitution enables thorough quality control through comprehensive algorithmic assessment while maintaining high processing throughput capable of handling large-scale 3D asset libraries.
Data Source
AI summary
A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform certain operations. The operations can include obtaining a rendered image for a 3D-asset generated from a reference image of an object. The operations also can include generating, using a machine learning model, a color score for the rendered image based on a first color histogram for the rendered image and a second color histogram for the reference image. The operations additionally can include generating, using a deep learning model and a slice loss function, a texture score for the rendered image. The acts operations can include determining a quality score for the rendered image based on a predetermined quality threshold and a combination of the color score and the texture score. Other embodiments are described.