3D Shape Reconstruction Using Feature Point Reliability Correction
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Solution Overview
Problem
Existing methods for generating highly accurate three-dimensional shape data require significant computational resources due to iterative convergence processes, leading to inefficiencies.
Innovation Solution
An information processing apparatus that utilizes multi-viewpoint image data to generate and correct three-dimensional shape data by determining the reliability of feature points based on identification accuracy, and adjusts spatial positions using object shape data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative transformation of curved surface shape data is performed until error convergence to obtain highly accurate three-dimensional shape data, then measurement precision is improved, but productivity deteriorates due to tremendous calculation requirements
Solution Approach 1:
The patent applies preliminary action by performing reliability determination on feature points before the iterative transformation process. The system calculates reliability values based on identification accuracy from multiple viewpoint images, and uses these pre-calculated reliability values to guide the subsequent iterative refinement. This preliminary classification allows the system to focus computational resources on feature points that need correction, rather than uniformly processing all points through multiple iterations.
Solution Approach 2:
The patent implements local quality by treating different feature points differently based on their reliability values. High-reliability feature points are processed with standard iterative transformation, while low-reliability feature points receive additional correction using object shape data. This localized differentiation allows the system to improve overall measurement precision without applying excessive computational effort uniformly across all points, thereby maintaining productivity.
2Manufacturing precision
If iterative transformation is performed to converge shape error for high accuracy, then manufacturing precision is improved, but loss of time increases due to repeated calculations
Solution Approach 1:
The system performs preliminary action by calculating reliability values for all feature points before entering the iterative transformation process. This pre-processing step classifies feature points into high-reliability and low-reliability groups, enabling the subsequent iterative process to focus computational time on correcting only the low-reliability points using object shape data, thereby reducing total processing time while maintaining high accuracy.
Solution Approach 2:
The patent applies local quality by differentiating the processing approach for different feature points based on their reliability. Instead of uniformly applying time-consuming iterative transformations to all points, the system selectively applies correction to low-reliability feature points using object shape data, while high-reliability points require minimal processing. This localized approach significantly reduces time loss while achieving the required manufacturing precision.
3Measurement precision
If feature points with low identification accuracy are corrected using object shape data, then measurement precision is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent applies local quality by implementing a selective correction mechanism that only processes low-reliability feature points. The system determines reliability for each feature point and applies object shape data correction only where needed, rather than uniformly processing all points. This localized correction approach improves measurement precision for problematic points while avoiding unnecessary processing complexity for already accurate points.
Solution Approach 2:
The patent implements parameter changes by using reliability values as a control parameter to gate the correction process. The reliability determination acts as a threshold parameter that changes the processing behavior: high-reliability points follow the standard iterative transformation path, while low-reliability points trigger additional correction using object shape data. This parameter-based control manages device complexity by dynamically adjusting processing intensity based on actual data quality needs.
Data Source
AI summary
Highly accurate three-dimensional shape data is obtained easily. The information processing apparatus obtains data of a plurality of captured images obtained by capturing an object from a plurality of viewpoints different from one another. Further, the information processing apparatus obtains first three-dimensional shape data indicating the shape of the object, which includes information indicating the spatial position of each of a plurality of feature points indicating features of the object. Furthermore, the information processing apparatus obtains second three-dimensional shape data indicating the shape of the object by using the captured image. Here, the information processing apparatus obtains, at the time of obtaining the first three-dimensional shape data, the first three-dimensional shape data, based on reliability of the spatial position of each of the plurality of feature points and the second three-dimensional shape data.