Branch-and-Bound 4D Camera Localization via Multi-Cone Alignment
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Solution Overview
Problem
Existing approaches to camera pose estimation from a single reference image relative to a 3D point-set are inefficient in handling unknown 2D-3D correspondences, often relying on local optimization and restrictive assumptions, which limits their ability to find optimal solutions without prior pose information.
Innovation Solution
The method involves generating a multi-cone structure based on key-points and intrinsic camera parameters, aligning it with a nominal alignment vector to reduce rotational degrees of freedom, and using a branch-and-bound search algorithm to determine a rigid transformation that maximizes the number of cones containing points in a 3D point cloud, thereby estimating the camera pose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If local optimization approaches are used for camera pose estimation, then computational complexity is reduced, but the ability to find optimal solutions deteriorates due to sensitivity to outliers and restrictive assumptions
Solution Approach 1:
The patent segments the search space into multiple regions or levels, allowing the algorithm to systematically explore different areas rather than relying on a single local optimization process. This segmentation enables the method to escape local optima while maintaining computational efficiency through hierarchical or divided search strategies.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting search parameters, threshold values, or optimization criteria during the pose estimation process. This allows the algorithm to adapt to different data conditions and overcome the restrictive assumptions of fixed-parameter local optimization methods, thereby improving solution optimality without excessive computational burden.
2Ease of operation
If prior pose information or restrictive assumptions are imposed, then the problem becomes more tractable, but the adaptability to unknown correspondences deteriorates
Solution Approach 1:
The patent introduces dynamics by making the pose estimation process adaptive rather than static. The algorithm dynamically adjusts its constraints and assumptions based on the actual data observed, allowing it to handle unknown correspondences effectively. This dynamic approach maintains problem tractability while improving adaptability to varying conditions.
Solution Approach 2:
The patent changes parameters adaptively during execution, adjusting the level of constraint or assumption based on the quality and characteristics of the input data. This allows the system to be more tractable when data is good and more adaptable when correspondences are unknown, resolving the contradiction between ease of operation and versatility.
3Measurement precision
If exhaustive search methods are used to find optimal correspondences, then solution optimality is improved, but computational time increases significantly
Solution Approach 1:
The patent segments the exhaustive search into manageable parts or levels, exploring the most promising correspondences first while systematically covering the search space. This segmentation allows the algorithm to achieve near-optimal results without the full computational burden of complete exhaustion, balancing accuracy and time efficiency.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the data, pre-identifying potential correspondences, or pre-establishing constraints before the main search process. This preliminary work reduces the effective search space, allowing optimal correspondence finding to be achieved faster without sacrificing accuracy.
Data Source
AI summary
In some implementations, a method includes: extracting key-points from a reference image associated with a scene; generating a multi-cone structure including cones based on the key-points and intrinsic parameters, wherein an apex of the cones corresponds to a camera origin point associated with the reference image, and wherein each axis of the cones intersects a respective key-point; aligning the multi-cone structure based on a nominal alignment vector in order to restrict two rotational degrees of freedom; and determining a rigid transformation for the multi-cone structure, including a value for a remaining rotational degree of freedom and values for three translational degrees of freedom, within a search space associated with the scene, wherein a number of the cones within the multi-cone structure that include at least one point in a three-dimensional (3D) point cloud associated with the scene is maximal when the multi-cone structure is transformed by the rigid transformation.