HMD Calibration Using Mobile Device Camera
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Solution Overview
Problem
Head-mounted devices (HMDs) often lose calibration due to physical impacts, requiring users to visit specialized locations for recalibration, which is inconvenient and time-consuming.
Innovation Solution
A method that uses a second device's camera as a proxy for the user's eye to assess and adjust the calibration of HMD components by comparing actual and expected images, determining the need for recalibration, and adjusting the HMD's components to restore calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users take their HMD to a factory store or specialized location for calibration, then calibration accuracy is improved, but user convenience deteriorates and time is lost
Solution Approach 1:
A mobile device serves as an intermediary tool between the user and the HMD calibration process. The mobile device captures images of calibration patterns and transmits them to the HMD, enabling calibration without requiring specialized equipment at factory stores. This intermediary approach maintains calibration accuracy while dramatically improving user convenience by allowing calibration to be performed anywhere with a standard mobile device.
Solution Approach 2:
The system uses a copy of the calibration pattern displayed on a mobile device screen instead of requiring specialized physical calibration equipment. The mobile device screen acts as a portable calibration target that can be viewed through the HMD, replicating the function of specialized calibration equipment while being universally accessible through standard mobile devices.
2Reliability
If users visit specialized locations for HMD calibration, then calibration quality is improved, but time consumption increases
Solution Approach 1:
The HMD performs calibration autonomously using images captured by a mobile device and processed by the HMD's own image processing capabilities. The system automatically detects calibration patterns, calculates alignment parameters, and adjusts internal calibration data without requiring technician intervention. This self-service approach maintains calibration quality while reducing time consumption from hours at a specialized location to minutes at home.
Solution Approach 2:
The mobile device displays calibration patterns and positioning instructions before the actual calibration measurement begins. The system prepares the calibration environment by showing users what to look for and how to position the mobile device, ensuring optimal conditions are established beforehand. This preliminary preparation enables accurate calibration to be performed quickly without requiring specialized calibration environments.
3Measurement precision
If specialized calibration equipment is used, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The mobile device performs multiple functions: it displays calibration patterns, captures images through the HMD optical system, and transmits data to the HMD for processing. This universal approach replaces specialized calibration equipment with a multi-functional device that users already possess, maintaining measurement accuracy while eliminating the need for complex dedicated calibration instruments.
Solution Approach 2:
The system creates a digital copy of the calibration process using software-based calibration patterns displayed on a mobile device screen, replacing physical specialized calibration equipment. This virtual calibration target can be dynamically generated and adjusted through software, eliminating the need for manufactured physical calibration artifacts while maintaining measurement precision through careful pattern design and image processing.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that assess calibration between eye tracking and other components of a head-mounted device (HMD) using another device, such as a mobile device. For example, an example process may include obtaining first sensor data captured by a first sensor of a first device, the first sensor data including a representation of a portion of a second device, obtaining second sensor data captured by a second sensor of the second device, detecting a position of the first sensor of the first device based on the second sensor data, and assessing a calibration between the portion of the second device and the sensor of the second device based on the first sensor data and the detected position of the first sensor.