Camera Pose Estimation via Synthetic Image Correlation
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Solution Overview
Problem
Existing camera pose estimation methods are prone to errors due to low frequency sampling and interference, often requiring a minimum number of 3D and 2D correlations that may not be available, leading to incomplete camera position determination in aerial video and image registration.
Innovation Solution
A method and system that estimates camera position using pattern matching, metadata interpretation, and synthetic 3D image rendering, correlating 2D images with synthetic 2D images to identify points of correlation and refine camera position through algorithms like SIFT, SURF, and POSIT, even with limited information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware camera tracking system is used, then camera position can be determined, but the position data is erroneous due to low frequency sampling and interferences
Solution Approach 1:
The patent introduces an intermediary system consisting of a tracking marker with known 3D coordinates and computer vision algorithms as a mediator between the camera and the tracking system. This intermediary enables more reliable position estimation by providing visual correlation points that can be processed at higher frequencies than hardware trackers, thereby improving both measurement precision and reliability
Solution Approach 2:
The patent replaces the hardware-based mechanical/optical tracking system with a computer vision-based system using image processing and pattern recognition. This substitution allows for higher frequency sampling and reduces interference issues, improving the reliability of position data while maintaining measurement precision
2Measurement precision
If traditional pose estimation algorithm is used, then camera pose can be computed, but it requires a minimum number of 3D and 2D correlations that may not be available
Solution Approach 1:
The patent applies partial action by using only the necessary subset of correlation points available in the scene rather than requiring the full minimum number. The system processes available 3D-2D correlations incrementally and uses optimization techniques to derive accurate pose information from fewer than the traditional minimum required points, thereby improving adaptability while maintaining precision
Solution Approach 2:
The patent changes the parameters of the pose estimation algorithm by modifying the correlation requirements and using alternative mathematical approaches that can work with fewer correlation points. This allows the system to adapt to limited data availability while still achieving accurate camera pose estimation
3Measurement precision
If more correlation points are used, then camera position accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent applies preliminary action by pre-defining tracking markers with known 3D coordinates and pre-processing the scene to identify potential correlation points before pose estimation. This preparation reduces the complexity of real-time processing while maintaining high accuracy, as the system only needs to match pre-characterized features rather than searching for correlations from scratch
Data Source
AI summary
A camera pose estimation system is provided for estimating the position of a camera within an environment. The system may be configured to receive a 2D image captured by a camera within the environment, and interpret metadata of the 2D image to identify an estimated position of the camera. A synthetic 2D image from a 3D model of the environment may be rendered by a synthetic camera within the model at the estimated position. A correlation between the 2D image and synthetic 2D image may identify a 2D point of correlation, and the system may project a line from the synthetic camera through the 2D point on the synthetic 2D image rendered in an image plane of the synthetic camera such that the line intersects the 3D model at a corresponding 3D point therein. A refined position may be determined based on the 2D point and corresponding 3D point.