3D Model Rendering Parameter Adjustment via Image Comparison
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Solution Overview
Problem
Current 3D object data model rendering techniques lack the ability to dynamically adjust rendering features based on reference images and user feedback, resulting in suboptimal visual representations of objects.
Innovation Solution
A method and system that receive a 3D object data model, compare its initial rendering to reference images, modify parameters based on image attributes, render the model again, and iteratively adjust parameters using statistical feedback to refine the appearance, allowing for dynamic adaptation of rendering features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D object data model rendering is performed using traditional techniques, then the rendering process is simple and fast, but the visual representation accuracy and realism are insufficient
Solution Approach 1:
The patent implements an iterative rendering system where rendered images are compared with reference images, and the differences are fed back to adjust rendering parameters. This feedback loop continuously refines the visual representation accuracy by comparing generated images against ground truth and automatically adjusting material properties, lighting, and geometry parameters to reduce discrepancies.
Solution Approach 2:
The system dynamically modifies multiple rendering parameters including material properties (reflectivity, roughness, transparency), lighting conditions, camera parameters, and geometric details based on the comparison results. By changing these parameters iteratively, the system achieves high visual accuracy without requiring complex manual intervention.
2Measurement precision
If rendering parameters are manually adjusted to improve visual accuracy, then the appearance quality improves, but the time consumption and operational complexity increase
Solution Approach 1:
The system performs automatic parameter adjustment without requiring manual intervention. The automated comparison between rendered and reference images triggers self-service optimization where the system independently identifies discrepancies and adjusts parameters to correct them, eliminating time-consuming manual tuning while maintaining high appearance quality.
Solution Approach 2:
The automated feedback mechanism continuously monitors rendering output against reference images and automatically adjusts parameters based on measured differences. This closed-loop system eliminates the need for iterative manual adjustment by using computational algorithms to process feedback and generate optimized parameters automatically.
3Measurement precision
If multiple rendering iterations are performed to improve accuracy, then the visual representation improves, but the computational time and processing resources increase
Solution Approach 1:
The system intelligently modifies rendering parameters based on the specific discrepancies detected between rendered and reference images. By adapting parameter adjustments to the actual errors found, the system achieves high accuracy with fewer unnecessary iterations, optimizing the balance between visual quality and computational efficiency.
Solution Approach 2:
The system performs partial iterations focusing only on adjusting parameters that show significant discrepancies between rendered and reference images. Rather than exhaustively testing all possible parameter combinations, the system applies targeted adjustments to the most impactful parameters, achieving sufficient accuracy with reduced computational overhead.
Data Source
AI summary
System and methods for rendering three-dimensional (3D) object data models based on a comparison of images. A 3D object data model of an object can be characterized by parameters defining rendering features of the 3D object data model. A comparison can be made of a first rendering of the 3D object data model to one or more reference images related to the object and, based on the comparison, the parameters of the 3D object data model can be modified. Following the modification, the 3D object data model can be rendered to generate a second rendering. Based on the second rendered 3D object data model, statistical information can be obtained and based on the statistical information, the parameters of the 3D object data model can be modified again to further adjust the appearance of the second rendering of the 3D object data model.