3D Model Generation via Multi-Measurement Optimization
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Solution Overview
Problem
Existing model fitting methods for generating three-dimensional models of objects often suffer from insufficient accuracy due to variations in image capture conditions, leading to suboptimal modeling results.
Innovation Solution
A modeling system that obtains a standard three-dimensional model and multiple sets of measurement data, using a model generating unit to optimize a predetermined evaluation function based on the data, thereby improving modeling accuracy by compensating for inaccuracies across multiple measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stereoscopic measurement is performed once to generate a three-dimensional model, then the modeling process is efficient and quick, but the measurement accuracy is insufficient leading to poor modeling quality
Solution Approach 1:
The patent segments the measurement process into multiple independent measurement sessions, where each session captures three-dimensional data from different angles and conditions. Instead of relying on a single measurement, the system divides the data collection into multiple parts that are subsequently integrated, allowing each individual measurement to be simpler while the collective result achieves high accuracy.
Solution Approach 2:
The patent merges multiple measurement data sets obtained from different measurement sessions into a unified three-dimensional model. By combining the strengths of multiple measurements and using optimization techniques to integrate them, the system achieves both high efficiency (through parallel measurement collection) and high accuracy (through data fusion and optimization).
2Manufacturing precision
If multiple measurement sessions are performed to improve accuracy, then modeling precision improves, but the time and complexity of the measurement process increases
Solution Approach 1:
The patent performs preliminary actions by collecting measurement data from multiple sessions in advance, storing them for later processing. This allows the actual model generation to use pre-collected data, reducing the critical path time. The system prepares multiple data sets beforehand through efficient measurement sessions, then processes them using optimization algorithms to achieve high accuracy without extending the overall workflow excessively.
3Manufacturing precision
If multiple measurement data sets are collected and processed, then modeling accuracy improves through averaging and weighting, but the computational complexity increases
Solution Approach 1:
The patent changes parameters by introducing weighting factors and optimization criteria when processing multiple measurement data sets. Instead of simple averaging, the system applies parameter-based weighting that prioritizes higher quality measurements and uses optimization algorithms to determine the best fit model. This approach manages computational complexity by focusing processing effort on critical parameters rather than uniformly processing all data equally.
Data Source
AI summary
The present invention provides a modeling technique with improved modeling accuracy. A modeling system obtains a plurality of pieces of measurement data by measuring an object a plurality of times, and obtains a standard model as a standard three-dimensional model of the object. The modeling system deforms the standard model so as to optimize a predetermined evaluation function including an evaluation element on the plurality of pieces of measurement data, thereby generating a three-dimensional model of the object.