HUD Ghost Image Measurement Using Multi-View Depth Calibration
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Solution Overview
Problem
There is a need for a method and apparatus to measure the optical characteristics of virtual images produced by augmented reality (AR) devices, including calculating parameters such as virtual image distance, look down/up angle, horizontal/vertical field of view, static distortion, and ghosting level.
Innovation Solution
A method and apparatus using multiple cameras positioned around a measurement reference point to capture a test image on a virtual plane, calculating coordinates and optical characteristics based on field of view (FOV) information and camera arrangement, employing equations to determine parameters like virtual image distance, look down/up angle, horizontal/vertical FOV, and ghosting level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to capture test images from different positions, then measurement precision of optical characteristics is improved, but device complexity increases
Solution Approach 1:
The measurement system is segmented into multiple independent camera units positioned at different locations (first camera at first position, second camera at second position, etc.). Each camera captures test images from its specific viewpoint, allowing the system to measure optical characteristics from multiple angles simultaneously. This segmentation enables comprehensive measurement of virtual image positions and ghost image characteristics without requiring a single complex measurement apparatus.
2Measurement precision
If coordinates of patterns are calculated using multiple captured images and FOV information, then accuracy of optical characteristic measurement is improved, but calculation complexity increases
Solution Approach 1:
Field of view (FOV) information serves as an intermediary element that bridges the captured images from multiple cameras and the final coordinate calculation. The FOV information, which includes angular measurements and spatial relationships, mediates the transformation of image data into accurate virtual plane coordinates. This intermediary approach simplifies the overall calculation process by providing a standardized reference framework for integrating data from multiple camera viewpoints.
Data Source
AI summary
An optical characteristic measurement method according to embodiments may comprise the steps of: generating multi-view images, the multi-view images including a left image, a central image, and a right image; displaying the position of a virtual image for the multi-view images; calculating the depth of the position of the virtual image; estimating the depth of the virtual image; matching the depth of the position to the depth of the virtual image; and calibrating errors between the multi-view images on the basis of the matched depth. Additionally, the optical characteristic measurement method may comprise the steps of: generating multi-view images, the multi-view images including a left image, a central image, and a right image; displaying the positions of virtual images for the multi-view images; estimating the depths of the positions of the virtual images; grouping the positions on the basis of the depths; selecting some positions from the grouped positions; and projecting a corrected virtual object on the basis of the selected positions and the depths.