Markerless AR 3D Reconstruction Using Structured Light
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Solution Overview
Problem
Existing marker-less augmented reality systems face challenges in real-time 3D reconstruction, particularly in mobile applications, due to the need for expensive algorithms and lengthy processing times, which hinder their suitability for dynamic scenes and unprepared environments.
Innovation Solution
A marker-less augmented reality system using one-shot spatially multiplexed structured light with a DeBruijn encoded color grid pattern, which projects a static two-dimensional grid onto the scene, analyzes deformation, and employs local optimization strategies like Hamming distance minimization and special vote majority for color detection and correction to achieve rapid 3D reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If marker-less AR systems use expensive algorithms for 3D reconstruction, then measurement precision is improved, but productivity deteriorates due to lengthy processing times
Solution Approach 1:
The patent segments the 3D reconstruction process into distinct phases: projecting structured light patterns, capturing images, detecting intersections, and calculating positions. This segmentation allows each phase to be optimized independently, enabling real-time processing while maintaining accuracy through systematic breakdown of complex operations
Solution Approach 2:
The system performs preliminary actions by projecting structured light patterns with pre-defined geometric configurations before actual measurement. The pre-stored spatial information and pre-calibrated camera parameters enable rapid 3D reconstruction without requiring expensive post-processing algorithms, thus improving processing speed while maintaining measurement precision
2Measurement precision
If marker-based AR systems are used, then measurement precision is improved through fiducial markers, but ease of operation deteriorates due to unwanted objects in the view
Solution Approach 1:
The patent extracts the fiducial marker from the scene by using implicit geometric features of the environment itself as reference objects. Instead of placing physical markers in the scene, the system identifies and uses naturally occurring geometric structures, eliminating unwanted visual elements while maintaining pose estimation accuracy
Solution Approach 2:
The system makes the AR system universal by enabling both marker-based and marker-less operation modes. The structured light projection can work with any scene containing geometric features, allowing the system to adapt to different environments without requiring specific markers, thus improving ease of operation while maintaining measurement precision
3Adaptability or versatility
If SFM methods are used for marker-less AR, then adaptability is improved for unprepared environments, but productivity deteriorates due to initialization requirements
Solution Approach 1:
The system performs preliminary calibration and stores spatial information about the environment before actual AR tracking begins. This pre-processing creates a reference framework that enables rapid real-time tracking without requiring expensive initialization routines during operation, thus improving productivity while maintaining adaptability to unprepared environments
Solution Approach 2:
The patent implements dynamic tracking by continuously updating the 3D reconstruction based on new image data while maintaining correspondence with pre-stored spatial information. This dynamic approach allows the system to adapt to environmental changes and object movements in real-time without requiring re-initialization, thus improving both productivity and adaptability
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 system enables fast and efficient 3D reconstruction in real-time, reducing processing time to less than one second while maintaining accuracy, making it suitable for mobile and dynamic environments.
Implementation Method 1
using a projector to project structured light pattern on the scene
Implementation Method 2
using an imaging device to capture an image of the scene including the object and the structured light pattern
Data Source
AI summary
The disclosure relates to marker-less augmented reality methods which are operable in mobile applications and which involve an object-tracking method that uses projection and detection of a structured light pattern to track the location, orientation, and/or movement of an object in a scene.