Structured Light AR Depth Sensing via Pixel Disparity
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Solution Overview
Problem
Current augmented reality systems face challenges in accurately placing virtual objects in real-world scenes due to the need for precise depth information, which is often cumbersome to obtain and computationally intensive, especially on mobile devices.
Innovation Solution
The system employs a structured light approach using a light source and camera to project a pattern onto a scene, determining pixel disparities to generate a three-dimensional computer model, allowing for accurate placement of virtual objects with minimal computational burden and robustness against motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional depth sensing methods are used to obtain precise depth information, then measurement precision is improved, but device complexity and computational burden increase significantly
Solution Approach 1:
The patent replaces complex mechanical depth sensing systems with a photogrammetry-based optical system. Instead of using multiple cameras or time-of-flight sensors, the system uses a single camera to capture images with projected light patterns, then computes depth information through image processing and disparity analysis. This substitution of mechanical sensing with optical-field-based measurement resolves the contradiction by achieving accurate depth information without increasing device complexity.
Solution Approach 2:
The patent introduces structured light patterns as an intermediary element between the camera and the scene. By projecting known light patterns onto the scene and analyzing how these patterns deform when captured by the camera, the system can compute depth information. This intermediary light field approach enables precise depth measurement while keeping the hardware simple, as the light patterns carry the depth encoding information that would otherwise require complex sensors to obtain.
2Measurement precision
If complex image processing is performed to achieve accurate depth mapping, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-defining the light projection patterns and their expected deformations for different depth planes. Instead of performing complex iterative optimization during real-time processing, the system prepares depth maps and disparity information in advance based on the known geometry of the projected patterns. This preliminary computation reduces real-time processing requirements while maintaining high measurement precision.
Solution Approach 2:
The patent segments the depth measurement process into distinct stages: capturing images with projected patterns, computing disparities between projected and captured pattern positions, and generating depth maps from disparity information. This segmentation allows each stage to be optimized independently, reducing overall computational burden while maintaining accuracy. The segmentation also enables parallel processing of different image regions or patterns.
3Manufacturing precision
If high-resolution depth maps are generated for accurate virtual object placement, then manufacturing precision is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent applies local quality by generating depth information at the specific locations where it is needed for virtual object placement, rather than computing full high-resolution depth maps of entire scenes. The system computes disparity and depth information locally for regions containing objects of interest, allowing accurate virtual object placement without the computational burden of processing entire high-resolution scenes. This localized approach maintains placement precision while reducing overall computational requirements.
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 method enables efficient and accurate generation of depth information on mobile devices, allowing for seamless integration of virtual objects into real-world scenes with reduced computational resources and minimal motion artifacts.
Implementation Method 1
the system employs a structured light approach using a light source and camera to project a pattern onto a scene
Implementation Method 2
determining pixel disparities to generate a three-dimensional computer model
Data Source
AI summary
An augmented reality system having a light source and a camera. The light source projects a pattern of light onto a scene, the pattern being periodic. The camera captures an image of the scene including the projected pattern. A projector pixel of the projected pattern corresponding to an image pixel of the captured image is determined. A disparity of each correspondence is determined, the disparity being an amount that corresponding pixels are displaced between the projected pattern and the captured image. A three-dimensional computer model of the scene is generated based on the disparity. A virtual object in the scene is rendered based on the three-dimensional computer model.