Pose Tracking via 3D Segmentation and Motion Modeling
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Solution Overview
Problem
Users face difficulties in determining and configuring the appropriate device for consuming digital content, and existing technologies struggle to accurately detect and track the position and orientation of handheld display media for seamless content projection.
Innovation Solution
The system employs augmented reality functional nodes (ARFNs) that use 3D image processing and segmentation to detect the pose and orientation of a handheld display medium, constructing a 3D corner model and motion model to project content correctly, even as the medium moves, by using computing devices with processors and interface components like cameras and projectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D image processing and segmentation are used to detect pose and orientation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the 3D image data to identify and isolate the display medium from the surrounding environment. This segmentation enables precise pose detection by focusing computational resources on the relevant object rather than processing the entire scene, thereby improving measurement precision while managing device complexity through selective processing.
Solution Approach 2:
The patent introduces intermediate processing steps including 3D corner model construction and motion model estimation that act as mediators between raw image data and final pose detection. These intermediary representations simplify the complex task of direct pose estimation while maintaining high precision through structured data transformation.
2Adaptability or versatility
If motion model parameters are estimated to track moving display medium, then adaptability is improved, but computing resources increase
Solution Approach 1:
The system employs dynamic motion models that adapt to the movement characteristics of the display medium in real-time. By estimating motion model parameters from sequential frames and updating predictions dynamically, the system achieves high adaptability to various motion patterns while optimizing computing energy through efficient temporal prediction rather than repeated full pose detection.
Solution Approach 2:
The patent performs preliminary action by estimating motion model parameters in advance based on historical pose data. This preliminary motion estimation allows the system to predict future positions and prepare for upcoming tracking challenges, improving adaptability while reducing instantaneous computing energy requirements during actual pose detection.
3Ease of operation
If content projection adapts to display medium size and pose, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system implements feedback by continuously monitoring the detected pose and size of the display medium and using this information to dynamically adjust content projection parameters. This closed-loop control enables automatic adaptation to various display configurations, greatly improving ease of operation while managing device complexity through efficient feedback processing rather than complex manual configuration systems.
Data Source
AI summary
Techniques are described for tracking the pose of a surface based on a point set representing the surface. Sequential observed poses of the surface are used as the basis for creating a mathematical model of the motion of the surface. Future motion of the surface may be predicted based on the mathematical model.