ToF Configuration Control Circuitry for Depth Map Accuracy
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Solution Overview
Problem
Time-of-flight systems face challenges in dynamically adjusting configuration parameters to account for various situations, leading to quality issues such as noise, pixel saturation, and depth accuracy loss due to non-adapted settings, which are difficult to predict analytically.
Innovation Solution
A configuration control circuitry and method using a learning algorithm, comprising a first sub-module to estimate a measurement indicator from image and depth data and a second sub-module to determine optimal configuration parameters for improving subsequent time-of-flight measurements, dynamically adjusting parameters like output power, modulation frequency, and integration time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predetermined configuration parameters are used for the ToF system, then the system operation is simple, but the measurement precision deteriorates due to non-adapted settings causing noise, pixel saturation, and depth accuracy loss
Solution Approach 1:
The system performs self-diagnosis and self-adjustment by automatically analyzing measurement quality indicators and adapting configuration parameters without external intervention. The control circuitry monitors depth map quality metrics and autonomously modifies illumination power, modulation frequency, and integration time to maintain optimal measurement precision.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where measurement quality indicators from depth maps are continuously monitored and fed back to the configuration control circuitry. This feedback drives automatic adjustment of configuration parameters, creating a self-optimizing system that maintains high measurement precision through iterative refinement.
2Reliability
If configuration parameters are dynamically adjusted to improve measurement quality, then the measurement precision improves, but the device complexity increases due to the need for learning algorithms and real-time parameter adaptation
Solution Approach 1:
The control circuitry autonomously performs quality assessment and parameter optimization without requiring complex external control systems. The system self-evaluates measurement quality indicators and automatically adjusts configuration parameters, reducing the need for complex external intervention while maintaining high reliability.
Solution Approach 2:
The system pre-adjusts configuration parameters based on predicted measurement quality requirements before actual measurements are taken. By anticipating quality issues and proactively modifying parameters, the system prevents measurement degradation rather than reacting to it, improving reliability while managing complexity through predictive control.
3Adaptability or versatility
If complex dependencies among configuration parameters are analytically predicted, then the adaptability improves, but the difficulty of detecting and measuring increases due to the analytical complexity
Solution Approach 1:
The system replaces complex analytical prediction methods with empirical machine learning models. Instead of attempting to mathematically model the complex relationships between configuration parameters, the system uses trained neural networks that have learned these relationships from data, significantly reducing the analytical difficulty while maintaining high adaptability.
Solution Approach 2:
The system transforms the problem from analyzing complex parameter relationships to directly observing measurement quality indicators. By changing the approach from theoretical analysis to empirical quality-based adjustment, the system achieves high adaptability without the burden of complex analytical detection and measurement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time adaptation of configuration parameters, reducing quality issues and improving depth map accuracy by learning complex dependencies among parameters, enhancing the reliability of time-of-flight measurements.
Implementation Method 1
time-of-flight (ToF) systems are known. Such ToF systems are typically used for determining a distance to objects in a scene
Implementation Method 2
a gain of the pixels of the pixel array is modulated according to a demodulation signal which may be phase-shifted with respect to the modulation of the light wave
Data Source
AI summary
A configuration control circuitry for a time-of-flight system, the time-of-flight system including an illumination source configured to emit light to a scene and an image sensor configured to generate image data representing a time-of-flight measurement of light reflected from the scene.