Epipolar Geometry Matrix Computation Using Plane Filtering Constraint
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Solution Overview
Problem
Conventional methods for computing the epipolar geometry matrix are time-consuming due to the need for extensive calculations, especially when dealing with coplanar feature points, which require repeated estimations to find the most suitable median error for accurate 3D reconstruction.
Innovation Solution
The method involves filtering out coplanar feature points before computing the epipolar geometry matrix, using a planar transform matrix to determine if selected feature points are coplanar, and setting the median error value to a maximum if they are, thereby reducing computational steps and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional fundamental matrix computation methods are used to ensure accurate 3D reconstruction, then manufacturing precision is improved, but productivity deteriorates due to extensive repeated calculations
Solution Approach 1:
The patent applies preliminary action by filtering out coplanar feature points before performing fundamental matrix computation. This preliminary filtering step prevents unnecessary calculations with coplanar points that would not contribute to accurate 3D reconstruction, thereby improving computation speed while maintaining reconstruction accuracy.
Solution Approach 2:
The patent extracts and removes coplanar feature points from the computation set before performing fundamental matrix estimation. By taking out these problematic points that cause redundant calculations, the system achieves faster computation without sacrificing the accuracy needed for reliable 3D reconstruction.
2Manufacturing precision
If repeated fundamental matrix estimations are performed to handle coplanar feature points, then manufacturing precision is improved, but loss of time worsens due to extensive computational steps
Solution Approach 1:
The patent performs preliminary filtering of coplanar feature points before the repeated estimation process. This preliminary action eliminates the need for extensive repeated calculations to handle coplanar points, reducing computation time while preserving the accuracy required for correct epipolar geometry matrix estimation.
Solution Approach 2:
The patent extracts coplanar feature points from the computation set before performing repeated fundamental matrix estimations. By removing these points that would require extensive repeated processing, the system reduces time loss while maintaining the precision needed for accurate 3D reconstruction.
3Productivity
If all selected feature points are used for epipolar geometry computation, then manufacturing precision may be compromised due to coplanar points, but productivity is improved by reducing filtering steps
Solution Approach 1:
The patent extracts and removes coplanar feature points from the computation set before performing fundamental matrix estimation. This extraction ensures that only non-coplanar points are used, maintaining the precision required for accurate 3D reconstruction while improving computational efficiency by reducing the number of points to process.
Solution Approach 2:
The patent performs preliminary filtering to identify and remove coplanar feature points before the main computation process. This preliminary action prevents precision loss from coplanar points while maintaining productivity by establishing an optimized computation set in advance.
Data Source
AI summary
A method for rapidly building image space relation using plane filtering limitation is provided. The method comprises first getting a plurality of continuous image data taken from different shooting angles. Then, a plurality of feature points of the image data is initialized. Next, a number of feature points of those image data is randomly extracted and compared to determine whether the compared feature points are coplanar or not. When the selected feature points are not coplanar, an epipolar fundamental matrix is calculated according to the selected feature points. On the contrary, when the selected feature points are coplanar, a medium error value for all the feature points is set as a maximum value.