Camera Pose Estimation Using 2D Array Link Structure
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Solution Overview
Problem
Existing camera pose estimation technologies for mosaic-based omnidirectional imaging struggle to achieve precise 3D information reconstruction and accurate camera pose estimation, especially when considering the geometric structure of a 2D array camera system.
Innovation Solution
A method for estimating camera pose information that involves acquiring multi-view images from a 2D array camera system, forming a 2D image link structure, estimating an initial camera pose based on corresponding feature points, and reconstructing a 3D structure to minimize reprojection errors, while considering the structural characteristics of the imaging tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera pose estimation methods are used for mosaic-based omnidirectional imaging, then the process is simpler, but the accuracy of 3D information reconstruction and camera pose estimation deteriorates
Solution Approach 1:
The patent segments the camera system into multiple 2D array camera units, each capturing images from specific spatial positions. By dividing the omnidirectional imaging task into multiple localized camera views and processing them through structured 2D array geometry, the system achieves higher pose estimation accuracy while managing complexity through modular organization of camera units and their corresponding image processing pipelines.
2Manufacturing precision
If geometric information of 2D array structure is incorporated, then 3D information reconstruction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameter representation by explicitly incorporating 2D array geometric parameters (spatial coordinates, camera positions, and array structure constraints) into the pose estimation process. This parameter integration allows the system to leverage the regular geometric structure of 2D array cameras to improve 3D reconstruction accuracy while reducing the search space for optimization algorithms through structured constraints.
Solution Approach 2:
The patent implements feedback mechanisms where the estimated camera pose and 3D structure information are iteratively refined using reprojection error minimization. The system continuously compares projected 3D points with actual 2D image features, uses the error feedback to adjust pose estimates, and repeats the process until convergence, thereby improving accuracy through iterative optimization guided by geometric consistency feedback.
3Measurement precision
If pair of corresponding feature points are formed between adjacent images in 2D array, then initial camera pose estimation precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the 2D image link structure based on the known geometric arrangement of 2D array cameras. Corresponding feature points between adjacent images are identified and paired in advance using the structured spatial relationships, which reduces the computational burden during real-time pose estimation and accelerates processing while maintaining high precision.
Data Source
AI summary
Disclosed herein are an apparatus for estimating a camera pose using multi-view images of a 2D array structure and a method using the same. The method performed by the apparatus includes acquiring multi-view images from a 2D array camera system, forming a 2D image link structure corresponding to the multi-view images in consideration of the geometric structure of the camera system, estimating an initial camera pose based on an adjacent image extracted from the 2D image link structure and a pair of corresponding feature points, and estimating a final camera pose by reconstructing a 3D structure based on the initial camera pose and performing correction so as to minimize a reprojection error of the reconstructed 3D structure.