3D Model Image Matching for Accurate Camera Pose Estimation
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Solution Overview
Problem
Conventional image processing techniques such as Structure from Motion (SFM) and Neural Radiance Field (NeRF) are used separately and do not provide an end-to-end optimization for determining camera poses, limiting their effectiveness in user applications, and there is a lack of methods to efficiently find corresponding images from three-dimensional models for user interaction.
Innovation Solution
A method that combines SFM and NeRF techniques for end-to-end optimization, allowing a computing device to acquire a query image, match it with a three-dimensional model, select a target image based on similarity, and determine a target camera pose, thereby enabling fast and accurate camera pose determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SFM and NeRF techniques are used separately for three-dimensional reconstruction and image synthesis, then the individual techniques can be implemented with existing methods, but the overall effectiveness in determining camera poses is limited and no end-to-end optimization is achieved
Solution Approach 1:
The patent combines SFM and NeRF techniques into a unified pipeline where SFM performs initial three-dimensional reconstruction to generate a coarse three-dimensional model, and NeRF performs detailed image synthesis based on this model. This merging allows end-to-end optimization for camera pose determination while leveraging the strengths of both techniques, resolving the contradiction between reliability improvement and system complexity.
2Productivity
If conventional separate techniques are used for image processing, then implementation is simpler, but the ability to efficiently find corresponding images from three-dimensional models for user interaction is lacking
Solution Approach 1:
The patent implements a feedback mechanism where the system determines camera poses for query images by comparing them against synthesized images from the three-dimensional model, selects the best matching pose, and provides this information back to the user. This feedback loop enables efficient image matching while enhancing user interaction capabilities, allowing users to effectively query and interact with three-dimensional models.
3Measurement precision
If end-to-end optimization for camera pose determination is implemented, then effectiveness is improved, but the complexity of combining and optimizing multiple techniques increases
Solution Approach 1:
The patent segments the camera pose determination process into distinct stages: SFM performs initial three-dimensional reconstruction to establish geometric structure, and NeRF performs subsequent image synthesis and pose optimization. This segmentation allows each technique to focus on its strength while enabling end-to-end optimization, improving camera pose accuracy without overwhelming complexity.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for determining a camera pose for an image. The method includes: acquiring a query image for a target object. The method further includes: acquiring a set of images from a three-dimensional model for the target object. The method further includes: selecting a target image from the set of images based on similarities between images in the set of images and the query image. The method further includes: determining a target camera pose corresponding to the query image based on a camera pose for the target image. The method enables fast and accurate determination of a camera pose for a query image from a three-dimensional model for a target object, thus increasing the efficiency of acquiring the camera pose and improving the user experience.