Differentiable Phase Correlation for 3D Observation Registration
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Solution Overview
Problem
Existing technologies face challenges in registering three-dimensional observations from heterogeneous sensors, particularly in pose registration tasks without initial values, due to high degrees of freedom and susceptibility to local optima.
Innovation Solution
A heterogeneous three-dimensional observation registration method based on depth phase correlation, utilizing pre-trained 3D U-Net networks for feature extraction and a differentiable phase correlation solver to estimate rotation, scaling, and translation transformations, enabling end-to-end training and registration of three-dimensional observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning-based methods are used for three-dimensional observation registration, then registration accuracy can be improved, but the methods are prone to local optima and rely on heuristically defined correspondences
Solution Approach 1:
The patent replaces traditional mechanical optimization methods with a differentiable phase correlation solver that uses gradient-based optimization. This substitution allows the system to navigate the high-dimensional pose space more effectively, avoiding local optima by utilizing smooth gradient information rather than relying on heuristic correspondences or iterative mechanical adjustment.
Solution Approach 2:
The patent transforms the registration problem by changing parameters through differentiable operations. By making the phase correlation solver differentiable and embedding it in an end-to-end learning framework, the system can continuously adjust parameters (rotation, scaling, translation) through gradient descent, enabling reliable convergence to global optima even in high-dimensional pose registration tasks.
2Adaptability or versatility
If the number of degrees of freedom is increased for three-dimensional pose registration, then registration capability is improved, but the complexity of the registration task increases
Solution Approach 1:
The patent segments the complex 7-degree-of-freedom pose registration task into separate translation and rotation components. By decoupling these transformations and handling them through differentiable operations in an end-to-end framework, the system reduces the effective complexity of optimizing all parameters simultaneously, making the high-dimensional registration problem manageable.
Solution Approach 2:
The patent leverages the differentiable phase correlation solver to operate in a transformed parameter space. By converting the registration problem into a differentiable optimization task in a higher-dimensional space, the system can efficiently navigate the complex pose manifold and achieve accurate registration despite the increased number of degrees of freedom.
3Device complexity
If existing two-dimensional image registration methods are applied to three-dimensional observations, then method simplicity is maintained, but registration accuracy deteriorates due to insufficient handling of three-dimensional transformations
Solution Approach 1:
The patent embeds the differentiable phase correlation solver within a broader end-to-end learning framework that handles three-dimensional transformations. This nested structure allows the system to maintain the simplicity of core correlation operations while wrapping them in sophisticated differentiable operations that accurately model 3D pose, scaling, and translation transformations.
Solution Approach 2:
The patent replaces traditional 2D image registration mechanisms with a 3D-aware differentiable phase correlation solver. This substitution enables the system to naturally handle three-dimensional transformations by computing gradients in 3D space, thereby achieving accurate registration without complicating the fundamental correlation-based approach.
Data Source
AI summary
Disclosed is a heterogeneous three-dimensional observation registration method, medium, and device based on depth phase correlation. The disclosure optimizes the phase correlation algorithm into a globally convergent differentiable phase correlation solver, and combines the solver with a simple feature extraction network, thereby a heterogeneous three-dimensional observation registration method whose overall framework is differentiable and capable of end-to-end training is established. The disclosure can achieve accurate three-dimensional observation registration for three-dimensional objects, scene measurements, and medical image data; and the registration performance is higher than the existing baseline model.