Stereoscopic Quality Metric for 3D Scene Distortion
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Solution Overview
Problem
Existing methods for assessing distortion in stereoscopic computer-generated scenes are qualitative, leading to inconsistent and suboptimal visual quality due to reliance on human inspection, which can result in over-distortion or under-correction in 3D visual effects.
Innovation Solution
A quantitative stereo-quality metric is calculated by computing a stereoscopic transformation of surface vertices, applying a translation vector and scale factor to obtain ghosted vertices, and then comparing these to the original vertices to determine the level of distortion, allowing for precise assessment and correction of perceived distortions in 3D scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human visual inspection is used to assess distortion, then the process is simple and qualitative, but the measurement precision and consistency are insufficient
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an automated computational system that performs stereoscopic transformations and calculates distortion metrics algorithmically, thereby improving measurement precision while maintaining operational simplicity
Solution Approach 2:
The patent introduces an intermediary computational metric (stereo-quality metric) that bridges the gap between complex stereoscopic distortion analysis and simple quality assessment, enabling precise measurement without requiring complex manual inspection procedures
2Productivity
If qualitative assessment methods are used, then the process is quick and easy, but the manufacturing precision of visual quality is insufficient
Solution Approach 1:
The system performs self-assessment by automatically computing distortion metrics without requiring external human inspection, thereby maintaining high productivity while achieving precise quality control through algorithmic evaluation
Solution Approach 2:
The patent replaces manual qualitative assessment with automated computational analysis that quickly calculates stereo-quality metrics, simultaneously improving both productivity and manufacturing precision of visual quality
3Ease of manufacture
If post-production manipulation is used to correct distortion, then the process is simple, but the manufacturing precision of visual quality may be compromised
Solution Approach 1:
The patent performs preliminary computation of distortion metrics before final rendering, allowing distortion correction to be applied during the modeling stage rather than through complex post-production manipulation, thereby maintaining both ease of manufacture and manufacturing precision
Data Source
AI summary
A computer-implemented method for measuring the stereoscopic quality of a computer-generated object in a three-dimensional computer-generated scene. The computer-generated object is visible from at least one camera of a pair of cameras used for creating a stereoscopic view of the computer-generated scene. A set of surface vertices of the computer-generated object is obtained. A stereoscopic transformation on the set of surface vertices is computed to obtain a set of transformed vertices. A translation vector and a scale vector are computed and applied to the set of transformed vertices to obtain a ghosted set of vertices. The ghosted set of vertices is approximately translational and scale invariant with respect to the set of surface vertices. A sum of the differences between the set of surface vertices and the set of ghosted vertices is computed to obtain a first stereo-quality metric.