ToF Camera Smart Binning for Motion-Accurate Depth Measurement
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Solution Overview
Problem
Time-of-flight cameras using amplitude-modulated continuous light face challenges in accurately measuring depth due to objects moving during the measurement process, leading to incorrect depth calculations for pixels near object edges, known as 'flying pixels', which are not effectively addressed by existing binning methods.
Innovation Solution
A method for depth measurement using a time-of-flight camera that involves acquiring sample sequences of amplitude samples at a higher sampling frequency than the modulation frequency, determining a confidence value for each pixel based on the correspondence of amplitude samples to a sinusoidal function, and employing 'smart binning' by weighting contributions of sample sequences according to their confidence values to calculate binned depth values, thereby reducing the impact of motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the 4-tap method is used for depth measurement, then the measurement speed is improved, but measurement precision deteriorates due to flying pixels caused by object motion
Solution Approach 1:
The patent divides the measurement process into multiple sample sequences (e.g., 4 taps) and processes each sequence independently to determine confidence values. By segmenting the depth map into multiple regions and evaluating each region's consistency, the method identifies and excludes flying pixels while maintaining high measurement speed through parallel processing of segmented data
Solution Approach 2:
The patent introduces confidence values as an intermediary metric to evaluate the reliability of depth measurements. This confidence map serves as a mediator between the raw depth data and the final processed output, allowing the system to identify unreliable measurements (flying pixels) and exclude them from the final depth map without reducing measurement speed
2Measurement precision
If multiple sample sequences are acquired and processed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary information (confidence values) from multiple sample sequences without requiring complex processing of all raw data. By taking out only the essential reliability metrics and using them to weight or exclude specific measurements, the method achieves high precision while keeping processing complexity manageable through selective data extraction rather than comprehensive analysis
Solution Approach 2:
The patent changes the parameter being measured from raw depth values to confidence values that indicate measurement reliability. This parameter transformation allows the system to process multiple sample sequences efficiently by evaluating a simplified metric (confidence) rather than complex depth relationships, reducing processing complexity while maintaining precision through confidence-based filtering
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
The method enhances the accuracy and reliability of depth measurements by filtering out unreliable data points, reducing the occurrence of incorrect depth values and blurring effects, and maintaining precise depth calculations even with moving objects.
Implementation Method 1
the camera emits a continuous field of amplitude-modulated light, which is reflected from objects in the field of view of the camera
Implementation Method 2
by the relative phase difference, the time-of-flight and thus the distance to the reflecting object can be determined
Implementation Method 3
The reflected light is received by the individual pixels
Data Source
AI summary
A method for depth measurement with a time-of-flight camera using amplitude-modulated continuous light by acquiring for each of a plurality of pixels of a sensor array of the camera at least one sample sequence having at least four amplitude samples (A0, A1, A2, A3) at a sampling frequency higher than a modulation frequency of the amplitude-modulated continuous light. The method further includes: determining for each sample sequence of each pixel a confidence value (C) indicating a degree of correspondence of the amplitude samples (A0, A1, A2, A3) with a sinusoidal time evolution of the amplitude; and determining for each of a plurality of binning areas, each of which comprises a plurality of pixels, a binned depth value (Db) based on the amplitude samples (A0, A1, A2, A3) of sample sequence of pixels from the binning area, wherein the contribution of a sample sequence to the binned depth value (Db) depends on its confidence value (C).