3D Model Visual Differential Scoring
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Solution Overview
Problem
Evaluating configuration changes in 3D data model generation systems is time-consuming and difficult due to subtle differences in the resulting models, making it hard to identify improvements or changes effectively.
Innovation Solution
A method that renders and compares 3D data models from different angles, calculating an object difference score based on image difference scores to quickly identify significant changes and guide users to areas of modification, thereby reducing manual testing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of 3D data models is performed, then evaluation accuracy is improved, but evaluation time increases significantly
Solution Approach 1:
The patent creates 2D image copies from 3D data models and compares these images to evaluate differences between models. By working with 2D representations rather than directly analyzing complex 3D geometries, the system enables faster automated comparison while maintaining evaluation accuracy through image-based difference metrics.
Solution Approach 2:
The patent replaces manual visual inspection with automated image processing and comparison algorithms. The system automatically renders images from 3D models, extracts features, computes difference scores, and identifies changes without human intervention, thereby reducing evaluation time while maintaining consistency and accuracy.
2Loss of information
If detailed comparison of all model regions is performed, then evaluation completeness is improved, but processing time increases
Solution Approach 1:
The patent applies different levels of detail to different regions of the 3D models based on their importance. The system identifies and prioritizes regions with significant differences or high visual importance, applying detailed analysis only to these critical areas while using coarser analysis for less important regions, thus maintaining completeness while reducing overall processing time.
Solution Approach 2:
The patent divides the 3D model into multiple regions or zones and evaluates them separately. By segmenting the model into manageable parts and processing each region independently, the system can identify changes efficiently without requiring exhaustive analysis of every pixel and vertex, thereby reducing processing time while preserving evaluation completeness.
3Productivity
If automated comparison methods are used, then processing speed is improved, but detection precision for subtle changes deteriorates
Solution Approach 1:
The patent employs multiple comparison parameters and metrics to detect both obvious and subtle changes in 3D models. The system varies parameters such as image resolution, feature extraction sensitivity, and difference thresholding to optimize detection precision for different types of changes while maintaining high processing speed through automated computation.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its comparison results based on detected changes. The automated comparison identifies potential differences, and the system can iteratively adjust comparison parameters or re-analyze specific regions to enhance detection precision for subtle changes while maintaining overall processing efficiency.
Data Source
AI summary
Methods and systems for rendering a three-dimensional (3D) data model of an object are provided. An example method may include receiving information associated with a first 3D data model and a second 3D data model of an object. The method may also include rendering a first set of images of the first 3D data model, and rendering a second set of images of the second 3D data model. The method may also include comparing respective images of the first set of images to images of the second set of images to determine a plurality of image difference scores between the respective images of the first set of images and the images of the second set of images. The method may also include determining an object difference score based on the determined plurality of image difference scores.