Multi-Projector Display Calibration Using GPU Frame Buffers
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Solution Overview
Problem
Existing multi-projector display systems face challenges in seamlessly blending overlapping outputs from multiple video projectors, leading to defects such as bright seams, gaps, and curvature distortions in the reconstructed image due to misalignment and differing projector characteristics.
Innovation Solution
A method utilizing a modifiable render buffer and view projection matrices to divide and transform a primary image into secondary images, which are then projected in an overlapping manner by multiple video projectors, ensuring a cohesive and seamless display on a projection screen through calibration data sets and graphics pipeline processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple video projectors are used to create a large virtual desktop display, then the display area and resolution are improved, but alignment errors and overlapping defects occur causing bright seams, gaps, and curvature distortions
Solution Approach 1:
The system performs preliminary calibration by projecting test patterns and capturing images with a camera to determine geometric transformation parameters before actual display operation. This advance preparation establishes the correction matrices needed to eliminate alignment errors, bright seams, and curvature distortions in the multi-projector display.
Solution Approach 2:
The patent replaces manual mechanical alignment and adjustment of multiple projectors with an automated digital image processing system. The system uses software-based geometric transformation and blending algorithms to correct alignment errors and overlapping defects, eliminating the need for precise mechanical positioning of each projector.
2Device complexity
If manual calibration methods are used for multi-projector alignment, then device complexity is reduced, but time consumption and operational difficulty increase
Solution Approach 1:
The calibration system is self-calibrating, automatically determining geometric transformation parameters by projecting test patterns, capturing images with a camera, and computing correction matrices without requiring manual intervention. This self-service approach eliminates time-consuming manual alignment while keeping the system relatively simple.
Solution Approach 2:
The system changes physical parameters during calibration by projecting test patterns at specific luminance levels and capturing images under controlled conditions. These parameter changes enable automatic determination of geometric transformation parameters, reducing calibration time while maintaining system simplicity.
3Area of stationary object
If overlapping projection regions are used to cover the entire display area, then coverage is improved, but bright seams and non-uniform luminance occur in the overlapping regions
Solution Approach 1:
The system applies local quality by using different blending strategies for different regions of the display. In overlapping regions, it applies luminance correction and blending based on the specific characteristics of each projector and screen region, while non-overlapping regions use direct projection without correction. This localized approach ensures uniform luminance across the entire display area.
4Manufacturing precision
If geometric transformation is applied to correct curvature distortions, then image accuracy is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The system segments the display area into multiple regions corresponding to each projector's coverage, with overlapping transition regions between them. This segmentation allows application of geometric transformation and blending operations on smaller, manageable regions rather than the entire display area, reducing processing complexity while maintaining image reconstruction accuracy.
Data Source
AI summary
A primary image is transformed into secondary images for projection, via first and second frame buffers and view projection matrixes. To do so, a first image is loaded into the first frame buffer. A calibration data set, including the view projection matrixes, is loaded into an application. The matrixes are operable to divide and transform a primary image into secondary images that can be projected in an overlapping manner onto a projection screen, providing a corrected reconstruction of the primary image. The first image is rendered from the first frame buffer into the second images, by using the application to apply the calibration data set. The second images are loaded into a second frame buffer, which can be coupled to the video projectors.


