Time-of-Flight Depth Computation Using Machine Learning
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Solution Overview
Problem
Time-of-flight cameras face errors and inaccuracies in depth computation due to multi-path interference and noise in raw sensor data, which complicates the processing and reduces the speed of obtaining accurate depth values, especially in real-time applications.
Innovation Solution
A trained machine learning component is used to directly compute depth values from raw time-of-flight sensor data, accounting for multi-path interference and noise in a single stage, eliminating the need for subsequent corrections and simplifying the processing, allowing for real-time accurate depth value generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-of-flight depth computation methods are used, then the processing speed is fast, but the measurement precision deteriorates due to multi-path interference and noise
Solution Approach 1:
The patent applies preliminary action by training a machine learning model in advance using simulated time-of-flight data with known ground truth depth values. The model learns to correct multi-path interference and noise artifacts before actual depth computation, enabling accurate depth estimation from noisy sensor data without complex real-time processing
2Measurement precision
If traditional multi-stage correction methods are used to improve depth accuracy, then the measurement precision improves, but the productivity deteriorates due to multiple processing stages
Solution Approach 1:
The patent merges multiple correction stages into a single machine learning model that performs noise reduction, multi-path interference correction, and depth computation simultaneously. This unified approach maintains high measurement precision while achieving real-time processing speeds by eliminating sequential processing bottlenecks
3Manufacturing precision
If simulated training data is used to train the machine learning component, then the manufacturing precision improves for depth computation, but the loss of information increases due to simulation-to-reality gap
Solution Approach 1:
The patent converts the simulation-to-reality gap from a harmful discrepancy into a beneficial feature by training the model on diverse simulated scenarios including extreme cases of multi-path interference. The model learns robust patterns that generalize well to real-world conditions, turning the abstraction of simulation into a strength for handling varied real-world scenarios
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 solution significantly improves the accuracy and speed of depth computation, reducing errors and enabling reliable performance in challenging scenarios like corners of rooms and highly reflective surfaces, making it suitable for real-time applications such as hand tracking and augmented reality.
Implementation Method 1
A TOF camera can be used to compute depth maps which contain information relating to the depth of an object in a scene from the camera. The depth refers to the projection of distance on an imaginary line that extends from the camera, where the distance is the absolute radial distance. A light source at the TOF camera illuminates the scene and the light is reflected by objects in the scene. The camera receives the reflected light that, dependent on the distance of an object to the camera, experiences a delay.
Data Source
AI summary
A depth detection apparatus is described which has a memory storing raw time-of-flight sensor data received from a time-of-flight sensor. The depth detection apparatus also has a trained machine learning component having been trained using training data pairs. A training data pair comprises at least one simulated raw time-of-flight sensor data value and a corresponding simulated ground truth depth value. The trained machine learning component is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained machine learning component.