Multi-Imager Camera Epipolar Plane Imaging 3D Reconstruction
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Solution Overview
Problem
Existing stereo image processing methods face challenges such as probabilistic uncertainties, errors, and high computational expense due to the lack of statistical redundancy in binocular stereo processing, leading to inaccurate and disjointed three-dimensional representations.
Innovation Solution
The system employs a multi-imager camera setup with multiple cameras arranged in a straight line, utilizing Epipolar-Plane Imaging (EPI) to capture redundant images. This allows for the application of linear filtering and statistical methods to estimate scene range accurately and precisely, reducing errors and computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If binocular stereo processing is used to reconstruct three-dimensional geometry from two images, then the system complexity is reduced, but the measurement precision and reliability deteriorate due to lack of statistical redundancy
Solution Approach 1:
The patent transitions from binocular stereo (two views) to multiview stereo by adding a temporal dimension. Instead of using only two simultaneous images from different cameras, the system uses multiple images from a single camera at different time points, effectively adding a time dimension to the observation space and creating statistical redundancy without increasing spatial complexity
Solution Approach 2:
The system performs preliminary actions by capturing multiple images at different time points before processing. This allows the system to accumulate statistical redundancy in advance, enabling more accurate three-dimensional geometry estimation through algorithms like Structure from Motion that leverage temporal variations in the images
2Manufacturing precision
If search-based correspondence methods are used to determine point correspondences between views, then the manufacturing precision is improved, but the computational expense increases
Solution Approach 1:
The patent applies continuity of useful action by using temporal sequences of images where correspondences can be established through continuous observation over time. Instead of discrete search-based matching between two views, the system leverages continuous temporal data to track correspondences, reducing computational search space while maintaining accuracy
Solution Approach 2:
The system uses feedback mechanisms where initial correspondence estimates are refined iteratively using temporal information. Algorithms like Structure from Motion provide feedback loops that continuously improve correspondence accuracy by leveraging the temporal coherence of the image sequence, reducing the need for computationally expensive search operations
3Manufacturing precision
If point cloud representations are used to represent three-dimensional scene descriptions, then the manufacturing precision is improved, but the homogeneity deteriorates leading to disjointed representations
Solution Approach 1:
The patent merges the advantages of point cloud precision with surface continuity by combining multiple point cloud estimates into coherent surface representations. By integrating temporal information across multiple images, the system merges discrete point estimates into continuous surface descriptions that maintain both precision and homogeneity
Solution Approach 2:
The system changes the representation parameters from discrete point cloud data to continuous surface parameterizations. By transforming the data representation from individual 3D points to continuous surfaces, the system maintains the precision of point estimates while achieving smooth, homogeneous scene descriptions that reflect the continuous nature of real-world objects
Data Source
AI summary
Systems and methods of the present disclosure can facilitate determining a three-dimensional surface representation of an object. In some embodiments, the system includes a computer, a calibration module, which is configured to determine a camera geometry of a set of cameras, and an imaging module, which is configured to capture spatial images using the cameras. The computer is configured to determine epipolar lines in the spatial images, transform the spatial images with a collineation transformation, determine second derivative spatial images with a second derivative filter, construct epipolar plane edge images based on zero crossings of second derivative epipolar planes image based on the epipolar lines, select edges and compute depth estimates, sequence the edges based on contours in a spatial edge image, filter the depth estimates, and create a three-dimensional surface representation based on the filtered depth estimates and the original spatial images.