AR Hand Pose Annotation Using 3D Calibration and Depth Capture
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Solution Overview
Problem
Existing methods for generating hand-related data annotations are labor-intensive, costly, and imprecise, particularly in determining depth coordinates, and synthetic images lack realism, limiting the accuracy and efficiency of training data for computer vision systems.
Innovation Solution
An augmented reality (AR) device with image sensors and processors generates 3-D hand models and captures images of users' hands in various poses, using 3-D modeling and real-time tracking to create accurate hand annotations without human intervention, ensuring realistic and diverse training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation methods are used, then annotation accuracy can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system uses the AR device itself to automatically capture and annotate hand pose data without requiring external manual annotation services. The device leverages its integrated sensors and processing capabilities to self-generate the annotation data needed for training models.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computer vision-based annotation. The system uses image sensors, depth information, and machine learning algorithms to automatically detect and annotate hand landmarks, substituting human annotators with an automated digital system.
2Productivity
If automated annotation methods are used, then productivity increases, but accuracy in capturing depth dimension may deteriorate
Solution Approach 1:
The system transitions from 2D image processing to 3D spatial understanding by incorporating depth information from AR sensors. This dimensional enhancement allows the automated system to accurately capture the depth dimension of hand poses while maintaining high productivity.
Solution Approach 2:
The patent introduces depth sensors and spatial mapping algorithms as intermediary components between the image capture and annotation processes. These intermediaries enable the system to accurately perceive and annotate three-dimensional hand geometry, bridging the gap between automated processing and measurement precision.
3Measurement precision
If manual annotation is used, then accuracy can be maintained, but cost and time efficiency deteriorate
Solution Approach 1:
The AR device performs multiple functions including capturing images, acquiring depth information, processing hand pose data, and generating annotations all within a single integrated system. This multi-functionality eliminates the need for separate manual annotation processes while maintaining accuracy through sophisticated algorithms.
4Adaptability or versatility
If real-world hand capture is used, then compatibility with AR scenarios improves, but data generation efficiency deteriorates
Solution Approach 1:
The system performs preliminary calibration and setup procedures to establish the AR device's coordinate system and hand tracking parameters before actual data collection. This preliminary configuration enables efficient real-world hand capture without requiring complex real-time adjustments, thereby improving both compatibility and efficiency.
Data Source
AI summary
An augmented reality (AR) device generates hand annotations for an image depicting a user's hand. The device includes a display, a processor, and a memory storing instructions for performing operations. The device performs a calibration operation to generate a 3-D model of the user's hand based on measurements. The calibration operation prompts the user to mimic the hand gesture presented by the animated virtual representation of a hand to obtain optimal images of the hand for hand pose estimation. A 3-D virtual representation of a hand in a hand pose corresponding with a hand gesture is generated based on the 3-D model. The device presents the 3-D virtual representation of the hand in AR via the display. During presentation, the device detects an input and captures an image of the user's hand positioned to correspond with the 3-D virtual representation. The captured image is stored with corresponding hand annotations based on the 3-D model.