Dynamic Depth Sensor Calibration via RGB-IR Fusion
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Solution Overview
Problem
Existing entertainment devices with RGB cameras and depth sensors, such as virtual reality devices, require costly and time-consuming factory recalibration due to physical expansion or contraction, leading to inaccurate depth maps, necessitating frequent recalibration.
Innovation Solution
A method that simultaneously captures RGB and IR images, calculates calibration data, and applies it to uncalibrated depth images to generate corrected depth images, allowing for on-device calibration and maintaining point-to-point correspondence between RGB cameras and depth sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory recalibration is performed to maintain point-to-point correspondence after physical expansion or contraction, then measurement precision is improved, but loss of time and manufacturing cost increase
Solution Approach 1:
The patent implements dynamic calibration by capturing calibration images at runtime using the device's own sensors rather than performing static factory recalibration. The system continuously monitors physical changes through sensor data and updates calibration parameters in real-time, transforming the recalibration process from a periodic factory operation to an ongoing dynamic adjustment that adapts to physical expansion and contraction without requiring device disassembly or specialized equipment.
Solution Approach 2:
The device performs self-calibration by using its own RGB cameras and depth sensors to capture calibration images and compute correction factors independently. The system automatically detects physical changes through inertial sensors and other device components, then adjusts its own calibration parameters without external intervention, eliminating the need for factory recalibration services and reducing time loss significantly.
2Measurement precision
If factory recalibration is performed to maintain point-to-point correspondence after physical expansion or contraction, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The device performs self-calibration by using its own RGB cameras and depth sensors to capture calibration images and compute correction factors independently. The system automatically detects physical changes through inertial sensors and other device components, then adjusts its own calibration parameters without external intervention, eliminating the need for factory recalibration services and reducing time loss significantly.
Solution Approach 2:
The patent replaces the mechanical factory recalibration process with a software-based computational approach. Instead of physically adjusting sensor positions or using specialized calibration equipment at the factory, the system uses image processing algorithms and sensor fusion to computationally correct for physical changes, substituting complex mechanical recalibration procedures with simpler software operations that can be performed automatically.
3Manufacturing precision
If traditional calibration methods are used, then point-to-point correspondence is achieved initially, but reliability deteriorates after physical expansion or contraction
Solution Approach 1:
The system implements continuous feedback by monitoring device physical state through inertial sensors, accelerometers, and other sensors that detect expansion, contraction, or positional changes. When physical changes are detected, the system automatically triggers recalibration routines to update correction factors, creating a closed-loop feedback mechanism that maintains calibration reliability over time rather than relying on static factory settings that degrade with physical changes.
Solution Approach 2:
The patent implements dynamic calibration by capturing calibration images at runtime using the device's own sensors rather than performing static factory recalibration. The system continuously monitors physical changes through sensor data and updates calibration parameters in real-time, transforming the recalibration process from a periodic factory operation to an ongoing dynamic adjustment that adapts to physical expansion and contraction without requiring device disassembly or specialized equipment.
Data Source
AI summary
A method, a system, and a computer program product for calibrating a depth sensor of a device. The method includes simultaneously capturing within a current scene: a red-green-blue (RGB) image by a RGB sensor of a device; and an infrared (IR) image by an IR sensor of the device. The method further includes concurrently capturing, via an IR sensor of the device, an uncalibrated depth image within the current scene. The method further includes calculating calibration data from the RGB image and the IR image and applying the calibration data to the uncalibrated depth image to generate a corrected depth image. The method further includes combining the RGB image and the corrected depth image to generate a RGB depth image. The method further includes, presenting the RGB depth image on an output device.