RGBD Camera Pose Refinement for Consistent Large-Scale 3D Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for large-scale 3D reconstruction using RGBD images lack a robust and accurate end-to-end framework for pose estimation, particularly in scenes with repeated patterns, lack of features, sudden camera movement, and multi-room settings, leading to inconsistent and inaccurate camera pose determination.
Innovation Solution
A method and system for determining camera poses in a global reference frame by computing relative poses between image frames, utilizing feature detection and matching, 3D feature filtering, and optimization techniques, with miscategorized pose detection and refinement processes to ensure consistency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pose estimation methods are used for large-scale 3D reconstruction, then the system can process RGBD images, but the camera poses are inconsistent and inaccurate in challenging scenarios
Solution Approach 1:
The patent segments the pose estimation process into distinct phases: feature detection, feature matching, relative pose computation, and global pose optimization. This segmentation allows each phase to be optimized independently, with feature detection focusing on robustness to repeated patterns, feature matching handling occlusions, relative pose computation addressing sudden camera movements, and global optimization ensuring consistency across all frames.
Solution Approach 2:
The patent transitions from 2D image feature matching to 3D point cloud-based pose estimation by incorporating depth information from RGBD sensors. This dimensional transition enables the system to leverage spatial structure and geometric constraints in 3D space, significantly improving pose accuracy and robustness compared to traditional 2D methods, especially in scenes with repeated patterns or limited texture.
2Measurement precision
If high accuracy camera pose estimation is achieved, then 3D reconstruction quality improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary feature detection and matching before pose computation, pre-identifying robust feature points and their correspondences across frames. This preliminary action filters out unreliable features early in the pipeline, reducing the computational burden during the actual pose estimation phase while maintaining high accuracy through selective use of only the most reliable feature matches.
Solution Approach 2:
The patent replaces traditional iterative optimization methods with closed-form solutions where possible, such as using essential matrix decomposition and fundamental matrix estimation to directly compute relative poses from feature correspondences. This substitution eliminates the need for complex iterative optimization in many cases, significantly reducing computational complexity while maintaining accuracy.
3Reliability
If robust pose estimation is achieved in scenes with repeated patterns and lack of features, then reliability improves, but measurement precision may be compromised
Solution Approach 1:
The patent applies different feature detection and matching strategies to different local regions of the image based on their characteristics. In regions with repeated patterns, it uses descriptors that are invariant to repetition, while in textureless regions, it relies on geometric constraints from depth information. This localized adaptation allows the system to maintain both robustness and precision across diverse scene conditions.
Solution Approach 2:
The patent combines multiple types of information - color features, depth information, and geometric constraints - to create a composite pose estimation approach. By fusing these different data sources, the system overcomes the limitations of any single modality, achieving both robustness to repeated patterns and high measurement precision even in challenging scenarios with limited visual features.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method of determining camera poses includes capturing a plurality of image frames using a camera, computing relative poses between each set of image frame pairs to provide a relative pose set and an uncategorized relative pose set, and detecting and removing miscategorized relative poses to provide a remaining relative pose set. The method also includes determining global poses using the remaining relative pose set and computing extended relative poses for at least a portion of the miscategorized relative poses and at least a portion of the uncategorized relative pose set to provide an extended relative pose set and an extended uncategorized relative pose set. The method further includes detecting and removing extended miscategorized relative poses to provide a remaining extended relative pose set and determining updated global poses for the plurality of image frames using the remaining relative pose set and the remaining extended relative pose set.