Structured-Light Pattern Optimization for 3D Reconstruction Accuracy
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Solution Overview
Problem
Achieving optimal performance in structured-light imaging systems is challenging due to the need for precise illumination patterns and decoding algorithms that are hardware-specific and computationally intensive, limiting accuracy and efficiency in 3D reconstruction.
Innovation Solution
A computer-implemented method and system that iteratively optimizes illumination patterns and reconstruction parameters using machine learning-based optimization to generate customized structured-light patterns and decoding algorithms, reducing reconstruction errors through stochastic gradient descent and derivative-free optimization, and incorporating user-defined constraints and image formation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional structured-light imaging methods are used, then the system is simpler to implement, but the 3D measurement accuracy is limited
Solution Approach 1:
The patent optimizes illumination patterns by adjusting control vectors that govern pattern parameters such as frequency, amplitude, and phase. By iteratively changing these parameters and evaluating reconstruction error, the system achieves higher 3D measurement accuracy without requiring fundamentally new hardware, thus resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent implements a feedback mechanism where the reconstruction error is continuously evaluated and used to update the control vectors and reconstruction parameters. This iterative feedback loop allows the system to progressively improve measurement accuracy by adapting illumination patterns based on actual reconstruction performance, maintaining system simplicity while enhancing precision.
2Measurement precision
If hardware-specific optimized illumination patterns are used, then measurement accuracy improves, but the system becomes less adaptable to different hardware configurations
Solution Approach 1:
The patent creates a universal optimization framework that can be applied across different hardware configurations. By formulating the optimization problem in terms of general control vectors and reconstruction parameters rather than hardware-specific constants, the system achieves hardware adaptability while maintaining high measurement accuracy through iterative optimization.
Solution Approach 2:
The patent employs dynamic optimization where control vectors and reconstruction parameters are iteratively updated based on feedback from reconstruction error evaluation. This dynamic adaptation allows the system to optimize for specific hardware configurations when needed while maintaining the capability to adapt to different hardware setups, thus resolving the contradiction between precision and adaptability.
3Measurement precision
If iterative optimization with multiple illumination patterns is used, then reconstruction accuracy improves, but computational time increases
Solution Approach 1:
The patent replaces computationally intensive brute-force search methods with gradient-based optimization techniques. By using gradient information to guide the search for optimal control vectors and reconstruction parameters, the system achieves high reconstruction accuracy significantly faster than exhaustive search, thus resolving the contradiction between precision and time loss.
Solution Approach 2:
The patent employs iterative optimization that stops when reconstruction error reaches a predetermined threshold, rather than exhaustively searching all possible patterns. This partial action approach achieves sufficient accuracy in reasonable time, balancing reconstruction accuracy with computational efficiency.
4Measurement precision
If complex decoding algorithms are used to achieve high accuracy, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent optimizes reconstruction parameters and control vectors through iterative evaluation of reconstruction error, achieving high accuracy through parameter optimization rather than complex algorithmic structures. This approach maintains algorithm simplicity while improving measurement precision through mathematical optimization.
Data Source
AI summary
There is provided a system and method for optimizing depth imaging. The method including: illuminating one or more scenes with illumination patterns; capturing one or more images of each of the scenes; reconstructing the scenes; estimating the reconstruction error and a gradient of the reconstruction error; iteratively performing until the reconstruction error reaches a predetermined error condition: determining a current set of control vectors and current set of reconstruction parameters; illuminating the one or more scenes with the illumination patterns governed by the current set of control vectors; capturing one or more images of each of the scenes while the scene is being illuminated with at least one of the illumination patterns; reconstructing the scenes from the one or more captured images using the current reconstruction parameters; and estimating an updated reconstruction error and gradient; and outputting at least one of control vectors and reconstruction parameters.