Flexible Device Bending Estimation for Depth Map Alignment
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Solution Overview
Problem
Inaccurate depth sensing due to bending of flexible devices in augmented and virtual reality systems, leading to misalignment errors and inefficiencies in computing resources.
Innovation Solution
A method for generating biometric signals based on bending estimation using a flexible device's bending data to authenticate users and rectify depth maps, employing a visual tracking system with inertial and optical sensors to track pose and generate reference biometric data for authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If flexible devices are used to provide adaptability to different users, then adaptability is improved, but measurement precision deteriorates due to bending-induced misalignment errors
Solution Approach 1:
The system continuously monitors bending estimates from visual tracking data and uses this feedback to dynamically adjust depth map calculations. The bending estimate serves as a correction factor that compensates for misalignment errors in real-time, allowing the system to maintain measurement precision despite device flexibility.
Solution Approach 2:
The system changes the parameter space by introducing bending estimates as an additional correction parameter. By modeling the relationship between device bending and depth sensing errors, the system transforms the problematic bending variable into a useful correction parameter that improves depth map accuracy.
2Measurement precision
If visual tracking systems continuously monitor bending to maintain accuracy, then measurement precision is improved, but use of energy increases due to continuous computational processing
Solution Approach 1:
The system applies partial correction by using bending estimates only for depth map adjustments rather than complete reprocessing. This selective application of correction reduces computational overhead while maintaining sufficient accuracy for authentication purposes.
Solution Approach 2:
The system performs preliminary bending estimation during normal visual tracking operations, capturing the necessary data during routine device use. This preliminary capture of bending information allows subsequent authentication processes to proceed with reduced computational requirements.
3Reliability
If bending data is collected and processed for authentication, then reliability of authentication is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The visual tracking system serves multiple functions: it performs its primary role of tracking user gaze and head position while simultaneously capturing bending data for authentication. This multi-functionality allows the system to extract authentication-relevant information from existing operational data without adding dedicated hardware or processing subsystems.
Solution Approach 2:
The system uses its own operational data (visual tracking information) to generate bending estimates for authentication purposes. Rather than requiring separate sensors or external systems, the device serves its own authentication needs by repurposing data already collected during normal operation.
Data Source
AI summary
A method for generating reference biometric data based on a bending of a flexible device is described. In one aspect, a method includes forming training data includes bending estimates of a flexible device worn by a first user, training a model based on the training data, and generating reference biometric data for the first user based on the model.