Camera Motion Determination Using 2D-3D Point Correspondence
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Solution Overview
Problem
Current methods for determining camera motion in three-dimensional imaging systems are computationally intensive and poorly suited for real-time applications, especially when dealing with obstructed points or planar regions, and require multiple calculations with large data sets.
Innovation Solution
A method that combines two-dimensional image data and three-dimensional point cloud data from a stereoscopic or multi-aperture camera system to efficiently determine camera motion by establishing point correspondence between two-dimensional pixels, allowing for the derivation of a rigid transformation between point clouds and enabling real-time camera motion determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factorization on established point correspondences between two-dimensional images is used to determine camera motion, then camera motion can be recovered, but the computation is intensive and difficult to perform in real-time
Solution Approach 1:
The patent segments the computation by separating point correspondence establishment (2D image data) from camera motion determination (3D point cloud registration). This division allows each step to be optimized independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter representation by using 3D point cloud coordinates derived from 2D correspondences rather than directly processing 2D image pixels. This transformation to 3D space simplifies the motion estimation problem and reduces computational burden.
2Measurement precision
If iterative closest point (ICP) method is used for three-dimensional registration of sequential point clouds, then camera motion can be determined, but it does not perform well with planar regions and requires multiple successive calculations
Solution Approach 1:
The patent performs preliminary action by establishing 2D point correspondences between consecutive images before performing 3D registration. This pre-established correspondence information guides the 3D point cloud registration, eliminating the need for iterative optimization and improving performance on planar surfaces.
Solution Approach 2:
The patent introduces 2D image correspondences as an intermediary to facilitate 3D point cloud registration. These 2D correspondences serve as a bridge that provides reliable initial alignment information, making the 3D registration process more robust and less computationally intensive.
3Measurement precision
If point correspondence is maintained over sequential images to determine camera motion, then motion can be recovered, but points may be obstructed or fall out of the image plane causing difficulties
Solution Approach 1:
The patent transitions from 2D image plane correspondence to 3D point cloud correspondence. By lifting points into 3D space, the system can maintain correspondence even when points move in and out of the 2D image plane or become occluded, as the 3D spatial relationships are preserved.
Solution Approach 2:
The patent creates a universal correspondence framework that works across multiple views and time steps by establishing correspondences in 3D space. This approach is more versatile than 2D correspondence, handling occlusions, viewpoint changes, and planar surfaces uniformly across different imaging conditions.
Data Source
AI summary
Camera motion is determined in a three-dimensional image capture system using a combination of two-dimensional image data and three-dimensional point cloud data available from a stereoscopic, multi-aperture, or similar camera system. More specifically, a rigid transformation of point cloud data between two three-dimensional point clouds may be more efficiently parameterized using point correspondence established between two-dimensional pixels in source images for the three-dimensional point clouds.