Camera Pose Estimation Using Feature Point Segmentation
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Solution Overview
Problem
Current methods for estimating camera pose in 3D stereoscopic image generation for virtual reality environments face challenges in accurately determining camera position and orientation, especially with mobile devices capturing non-uniform image paths, which can lead to computational inefficiencies and distortion in the final 3D images.
Innovation Solution
A computer-implemented method and system that uses a pose estimation module to detect feature points, track them across frames, and apply constraints like equidistant orbit and concentric optical axis constraints to reduce the search space for camera pose estimation from six degrees of freedom to four, coupled with gyroscope data for accurate pose determination, and a stitching module to generate distortion-free 3D images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point tracking and projection location comparison are performed for all feature points in non-uniform image paths, then measurement precision of camera pose is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the full set of feature points into multiple subsets, performing pose estimation on each subset separately. This division reduces the computational burden of processing all feature points simultaneously while maintaining overall estimation accuracy through aggregation of results from multiple subsets.
Solution Approach 2:
The patent performs pose estimation on a selected subset of feature points rather than all available feature points. This partial action approach achieves sufficient measurement precision for the application while significantly reducing computational complexity and processing requirements.
2Measurement precision
If the search space for camera pose estimation is maintained at six degrees of freedom, then measurement precision is preserved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the six-degree-of-freedom search space into multiple smaller search spaces corresponding to different feature point subsets. Each subset requires less computational effort to evaluate, reducing overall processing time while maintaining comprehensive coverage of the pose parameter space through aggregation of results.
Solution Approach 2:
The patent performs pose estimation on a subset of feature points rather than all feature points, which reduces the effective search space and computational requirements. This partial action approach achieves sufficient precision for practical applications while significantly reducing processing time.
3Adaptability or versatility
If initial camera poses are selected without constraints for non-uniform capture paths, then adaptability to different capture scenarios is improved, but distortion in final 3D images increases
Solution Approach 1:
The patent applies constraints to the selection of initial camera poses before performing pose estimation. This preliminary action ensures that even for non-uniform capture paths, the initial poses are chosen from a constrained set that is more likely to produce accurate results, thereby reducing image distortion while maintaining adaptability through the constraint framework.
Data Source
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AI summary
Systems and methods are described for estimating a camera pose. The estimation may include obtaining a sequence of images including a plurality of image frames of a scene, detecting a first set of feature points in a first image frame, and tracking the first set of feature points in a plurality of subsequent image frames. While continuing to track the first set of feature points, the estimation can include detecting a second set of feature points in a second image frame, tracking the second set of feature points, selecting a first initial camera pose associated with the first image frame and a second initial camera pose associated with the second image frame, determining projection locations, the projection locations based on the first initial camera pose and the second initial camera pose, and comparing the projection locations corresponding to each feature point in the first and second sets of feature points.