ToF Sensor Data Processing Segmentation for Precision Range Tradeoff
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Solution Overview
Problem
Current Time-of-Flight (ToF) camera systems face challenges in processing data in real-time due to the tradeoff between depth precision and unambiguous range, which is problematic for both situational awareness and high-fidelity recording, especially on devices with limited computational resources.
Innovation Solution
The system processes coarse depth data in real-time to generate a low-fidelity 3D representation or intensity image and stores both coarse and fine depth data for later processing, allowing for the generation of a high-fidelity 3D representation, using techniques such as dual-frequency modulation and coded modulation signals, and employs neural networks for prediction and post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher modulation frequency is used to improve depth precision, then depth/distance precision is improved, but unambiguous range is reduced
Solution Approach 1:
The patent segments the depth data processing into two distinct levels: coarse depth data processed in real-time for situational awareness, and fine depth data processed later for high-fidelity reconstruction. This segmentation allows the system to use different modulation frequencies appropriately - lower frequency for coarse data with adequate range, and higher frequency for fine data with superior precision - thereby resolving the fundamental tradeoff between precision and range.
Solution Approach 2:
The patent implements dynamic processing where the system adapts its processing strategy based on operational requirements. During real-time operation, only coarse depth data is processed to maintain low latency. When computational resources become available or high-fidelity output is required, the system dynamically processes the stored fine depth data to generate high-resolution 3D representations, thus optimizing performance across different operational states.
2Measurement precision
If real-time processing of fine depth data is performed to achieve high precision, then depth precision is improved, but computational resource requirements increase
Solution Approach 1:
The patent applies preliminary action by capturing and storing both coarse and fine depth data during the sensing phase without performing intensive processing at that moment. The fine depth data is preserved in raw or minimally processed form, allowing high-precision 3D reconstruction to be performed later when computational resources are available, thus avoiding the burden of real-time high-precision processing on resource-constrained devices.
Solution Approach 2:
The patent implements partial action by selectively processing only the necessary depth data at different stages. Coarse depth data is processed continuously in real-time for essential situational awareness, while fine depth data processing is performed partially or fully only when computational resources permit or when high-fidelity output is specifically required, optimizing the balance between precision and resource consumption.
3Device complexity
If lower frame rate or down sampling is used to reduce processing load, then computational resource requirements are reduced, but situational awareness capability deteriorates
Solution Approach 1:
The patent applies local quality by providing different quality levels of depth processing for different application needs. The system maintains high frame rates with coarse depth processing for real-time situational awareness and navigation, while enabling optional high-fidelity 3D reconstruction at lower frame rates when computational resources are available. This localized quality differentiation ensures that critical real-time functions are not compromised while still enabling high-precision outputs when needed.
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 enables real-time environmental awareness and high-fidelity reconstructions on devices with limited resources, maintaining suitable precision for situational awareness while allowing for detailed reconstructions when computational resources are available.
Implementation Method 1
continuous-wave ToF cameras measure the distance d via the phase difference φ between the emitted and reflected light by: d=(φ*c)/(4*π*fmod)
Implementation Method 2
One sensor that can be used for both purposes is the ToF camera
Data Source
AI summary
Systems and methods are provided for processing time of flight (ToF) data generated by a ToF camera. Such systems and methods may comprise receiving the ToF data comprising fine depth data and coarse depth data of an environment, processing the received coarse depth data in real time to generate one of a coarse three-dimensional (3D) representation or an intensity image of the environment, storing the received fine depth data and the coarse depth data, and processing the stored fine depth data and the coarse depth data, at a later time, to generate a fine 3D representation of the environment.