Time-Of-Flight Binning Using Vector Addition for Noise Reduction
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Solution Overview
Problem
Existing Time-Of-Flight measurement techniques face inaccuracies due to noise in binning methods, which affect the precision of phase and confidence data used for depth perception in TOF camera systems.
Innovation Solution
A method for binning Time-Of-Flight data that combines phase and confidence data using vector addition, allowing for temporal, spatial, or combined temporal and spatial binning, with predetermined confidence and movement or depth thresholds to enhance accuracy and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard binning methods are used to combine TOF data, then the number of measurements can be reduced, but noise in the measurements increases and accuracy decreases
Solution Approach 1:
The patent changes the parameter space by transforming individual phase and confidence measurements into a complex vector representation (I, Q components). Instead of directly averaging phase values, the method converts measurements to complex numbers, performs vector addition, and then extracts the binned phase from the resultant vector. This parameter transformation enables noise reduction while maintaining measurement speed.
Solution Approach 2:
The patent introduces complex vectors as an intermediary representation between raw phase measurements and binned results. Each measurement is converted to a complex vector with I and Q components, which serve as intermediate carriers that preserve both magnitude and phase information. The vector summation of these intermediaries produces a resultant that inherently filters noise, solving the contradiction between speed and accuracy.
2Measurement precision
If more measurements are taken to improve accuracy, then phase measurement precision improves, but the time required for measurement increases
Solution Approach 1:
The patent applies partial action by using a fixed, limited number of measurements (typically 4 phase shifts) rather than continuously increasing measurements. The vector-based binning method processes these partial measurements efficiently through complex number arithmetic, achieving high accuracy without requiring excessive measurement time. The confidence metric further optimizes this by determining when sufficient measurements have been collected.
3Ease of operation
If simple averaging of phase values is used, then the processing is fast, but the noise reduction effect is insufficient
Solution Approach 1:
The patent replaces the mechanical operation of simple arithmetic averaging with a vector-based complex number system. Instead of directly averaging phase angles (which is mathematically complex and noise-prone), the method substitutes this with vector addition in the complex plane. This substitution maintains computational efficiency while dramatically improving noise reduction through the geometric properties of vector summation.
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 significantly reduces noise in Time-Of-Flight measurements, providing more accurate binned phase data and improved depth perception by effectively managing data acquisition based on confidence and movement or depth thresholds.
Implementation Method 1
Each measured phase and its corresponding confidence value are associated with a complex vector, wherein the measured phase corresponds to a phase of the complex vector and the confidence value corresponds to a norm of the complex vector. The method comprises binning the plurality of complex vectors by adding the complex vectors to one another in order to obtain a binned complex vector.
Data Source
AI summary
The invention relates to a method for binning TOF data from a scene, for increasing the accuracy of TOF measurements and reducing the noise therein, the TOF data comprising phase data and confidence data, the method comprising the steps of acquiring a plurality of TOF data by illuminating the scene with a plurality of modulated signals; associating each modulated signal with a vector defined by a phase and a confidence data, respectively; adding the plurality of vectors for obtaining a binned vector; determining the phase and confidence of the binned vector; processing the phase and confidence data of the binned vector for obtaining depth data of the scene.