ToF Phase Unwrapping Using Lower-Dimensional Lookup Projection
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Solution Overview
Problem
In time-of-flight (ToF) imaging, the measured phase wraps every 2π, leading to ambiguous distance determination due to the periodic nature of modulated light, and noise in phase unwrapping computations can result in inaccurate distance measurements.
Innovation Solution
Utilize a matrix of points and a look-up table to determine a phase order set and associated depth value by projecting noisy phase measurements onto a lower dimensional plane and comparing them to a predetermined matrix of points, selecting the most likely phase order set using independent terms to reference the look-up table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phase unwrapping methods are used to determine depth values, then distance measurement can be achieved, but noise in phase measurements leads to inaccurate distance determination
Solution Approach 1:
The patent pre-calculates a matrix of points representing all possible phase order combinations and stores them in a look-up table before actual depth measurement. This preliminary preparation allows the system to quickly compare noisy phase measurements against pre-computed reference points, eliminating the need for complex real-time noise filtering and improving both accuracy and speed
Solution Approach 2:
The patent creates a simplified copy of the phase space by projecting the K-dimensional phase measurements onto a lower-dimensional plane and comparing them to a pre-computed matrix of reference points. This copying approach transforms the complex noise problem into a simpler pattern matching problem, where the system finds the closest match in the look-up table rather than attempting to directly process noisy measurements
2Measurement precision
If multiple modulation frequencies are used to resolve phase ambiguity, then distance determination accuracy improves, but computational complexity increases
Solution Approach 1:
The system pre-computes the entire matrix of points representing all possible phase order combinations across multiple frequencies and stores them in a look-up table. This shifts the computational burden from runtime to initialization, allowing complex multi-frequency phase unwrapping to be performed through simple table lookups during actual depth measurement
Solution Approach 2:
The patent transforms the K-dimensional phase space problem into a lower-dimensional comparison problem by projecting phase measurements onto a reduced dimensional plane. This dimensionality reduction simplifies the computational task while preserving the essential information needed to resolve phase ambiguity across multiple frequencies
3Measurement precision
If complex phase unwrapping algorithms are implemented to handle noisy phase data, then measurement accuracy improves, but processing speed decreases
Solution Approach 1:
By pre-calculating and storing the matrix of reference points in a look-up table, the system eliminates complex real-time computations during depth measurement. The runtime operation reduces to simple comparison and selection of the closest match, dramatically improving processing speed while maintaining accuracy
Solution Approach 2:
The patent creates a simplified computational model by copying the essential phase relationship information into a reduced-dimensional look-up table. This copying transforms the complex algorithm into a simple pattern matching operation, achieving fast processing without sacrificing measurement accuracy
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
Improves phase unwrapping efficiency with fast computation and low memory usage, providing accurate distance determination by disambiguating phase shift data despite noise.
Implementation Method 1
control the illumination source to output amplitude-modulated light at K modulation frequencies
Implementation Method 2
a distance to a point on an imaged surface in the environment is determined based on a length of a time interval in which amplitude-modulated light emitted by the depth imaging system travels out to that point and then returns back to a sensor
Data Source
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AI summary
One example provides a computing system comprising a depth sensor comprising a plurality of pixels, and a storage machine holding instructions executable by a logic machine to, for each pixel, make K phase measurements to form a set of noisy phase measurements, determine a location at which a projection line that passes through the set of noisy phase measurements in a K-dimensional phase space passes through a lower dimensional plane, the projection line being parallel to a noise free phase evolution line, compare the location to a plurality of independent terms of a predetermined matrix of points in the lower dimensional plane, locate a corresponding set of noiseless phase orders by using a selected set of independent terms to reference a look-up table, determine a distance value for the pixel based upon the corresponding set of noiseless phase orders, and output the distance value for the pixel.