3D Object Representation Accuracy Evaluation
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Solution Overview
Problem
Converting 3D object representations between different formats can introduce accuracy differences, making it challenging to evaluate and select the most suitable format for 3D printing, especially when formats like NURBS are converted to Steiner Patches or planar meshes, which affects modeling precision and memory usage.
Innovation Solution
A computer system compares 3D object representations by calculating sampling errors and modeling differences using grid points and random points to determine accuracy, allowing for the selection of the most accurate representation format based on memory constraints and desired precision, enabling conversions between NURBS, Steiner Patches, and planar meshes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If 3D object representations are converted between different formats (e.g., NURBS to Steiner Patches or planar meshes), then the representation becomes more suitable for 3D printing and easier to manipulate, but modeling precision and accuracy are reduced
Solution Approach 1:
The patent replaces manual visual inspection and subjective assessment of conversion accuracy with an automated computational system. The computer system calculates sampling errors and modeling differences using mathematical algorithms, substituting human judgment with precise computational measurement to evaluate the accuracy of format conversions.
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between the converted 3D object representation and the user. This system calculates and compares multiple metrics (sampling errors, modeling differences) to provide an objective assessment of conversion accuracy, enabling informed decisions about whether the converted representation meets required precision thresholds.
2Productivity
If different 3D object representation formats are used, then processing efficiency and memory usage improve, but accuracy differences are introduced
Solution Approach 1:
The patent implements a feedback mechanism where the computer system calculates accuracy metrics (sampling errors, modeling differences) for converted 3D representations and uses this information to guide format selection. The system provides feedback about the accuracy of conversions, enabling users to adjust conversion parameters or select alternative formats based on the measured performance.
Solution Approach 2:
The patent evaluates multiple representation formats by changing parameters such as mesh density, sampling rates, and geometric approximation levels. By systematically varying these parameters and measuring their impact on accuracy metrics, the system identifies optimal settings that balance processing efficiency with acceptable precision thresholds.
3Measurement precision
If multiple 3D object representation formats are evaluated and compared, then the most accurate format can be selected, but computational complexity and time increase
Solution Approach 1:
The patent segments the accuracy evaluation process into distinct computational components: calculating sampling errors separately from modeling differences, and evaluating different surface patches independently. This segmentation allows the system to process complex comparisons in manageable steps, reducing overall computational complexity while maintaining comprehensive accuracy assessment.
Data Source
AI summary
An example of a computer-readable medium is provided to store machine-readable instructions. The instructions may cause a processor to receive three-dimensional (3D) object representations of an object. The distance between points of a grid applied to 3D object representation and random points on the 3D object representation may provide a sampling error used in calculating a modeling accuracy between the 3D object representations.