Pre-stitching Tuning Automation for Panoramic VR
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Solution Overview
Problem
Current panoramic virtual reality (VR) systems face challenges in achieving seamless image stitching due to calibration differences between stereoscopic cameras, requiring manual adjustments that are time-consuming and costly, especially during live events.
Innovation Solution
An automated system that uses a single camera sensor rotated to capture both left and right eye images, with a reference area from one eye used to correct seam areas from the other eye, employing Lagrange transformations and disparity calculations to adjust pre-stitch parameters, such as color and alignment, for consistent panoramic views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of camera parameters is performed to ensure consistent images across panoramic view, then image consistency is improved, but time consumption and cost increase
Solution Approach 1:
The system performs self-calibration by automatically detecting feature points in overlapping seam areas between adjacent camera images and computing adjustment parameters without human intervention. The calibration process is autonomous, using the camera network itself to identify and correct parameter deviations, thereby eliminating the need for manual adjustment while maintaining image consistency.
Solution Approach 2:
The patent replaces manual mechanical adjustment of camera parameters with an automated computational system. Instead of physically adjusting camera lenses or sensors, the system uses image processing algorithms to detect seam area disparities and automatically computes parameter adjustments, substituting mechanical operations with digital signal processing and mathematical transformations.
2Measurement precision
If manual calibration is performed before live events, then calibration accuracy is improved, but adaptability to live event conditions deteriorates
Solution Approach 1:
The calibration system transitions from a static pre-event procedure to a dynamic real-time process. The system continuously monitors and adjusts camera parameters during live events by processing incoming image streams and computing adjustments on-the-fly. This dynamic approach allows the system to adapt to changing environmental conditions, camera movements, and lighting variations that occur during live broadcasting.
Solution Approach 2:
The system implements a feedback mechanism where seam area images are continuously analyzed to detect calibration deviations, and adjustment parameters are computed and applied in real-time. The feedback loop processes image data from the camera network, identifies disparities in overlapping regions, and automatically corrects parameter drift, enabling continuous adaptation to live event conditions while maintaining calibration accuracy.
3Area of stationary object
If multiple stereoscopic camera pairs are used to capture panoramic view, then coverage area is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal calibration framework that handles multiple camera pairs with different configurations (wide-angle, telephoto, varying focal lengths) through a single automated process. The system uses a common coordinate system and unified parameter adjustment methodology that works across diverse camera types, eliminating the need for separate calibration procedures for each camera pair and reducing overall system complexity.
Solution Approach 2:
The system introduces a central calibration server as an intermediary that coordinates calibration across multiple camera pairs. This intermediary receives images from all cameras, performs centralized computation of adjustment parameters using seam area analysis, and distributes corrected parameters back to individual cameras. The intermediary abstracts the complexity of multi-camera coordination, allowing each camera to operate independently while maintaining global calibration consistency.
Data Source
AI summary
Methods, systems and apparatuses may provide for technology that identifies a seam area between a pair of images corresponding to a first eye and determines a disparity between the seam area and a reference area at a center line of a reference image corresponding to a second eye. The technology may also automatically adjust one or more pre-stitch parameters of camera sensors associated with the pair of images and the reference image based on the disparity.