Stereo Image Calibration via Virtual Camera Rig Alignment
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Solution Overview
Problem
Existing stereo image production techniques often suffer from calibration errors and discrepancies in camera equipment, leading to viewer discomfort and inaccuracies in 3D video sequences due to imperfections in camera lenses, mechanics, and settings.
Innovation Solution
A computer-implemented method that processes pairs of left and right images to determine a new virtual camera rig, adjusting parameters such as tilt, roll, pan, and zoom to align with the actual camera settings, thereby reducing misalignment and preserving the specified convergence distance, using a virtual 3D space and marker-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional stereo image recording is used with standard camera equipment, then the production process is simple and straightforward, but calibration errors and discrepancies occur leading to viewer discomfort and inaccuracies in 3D video sequences
Solution Approach 1:
The system performs preliminary calibration by capturing test images and determining actual camera settings (focal length, principal point, baseline) before actual stereo recording. This preliminary action establishes accurate camera parameters that compensate for manufacturing tolerances and mechanical discrepancies, ensuring reliable 3D video sequences while maintaining simple operation during actual recording.
Solution Approach 2:
The system creates a virtual copy of the physical camera rig in software, including virtual cameras with the determined actual settings. This virtual model allows for precise control and adjustment of camera parameters without physically adjusting the expensive stereoscopic camera equipment, thereby improving accuracy while avoiding complex manual calibration procedures.
2Measurement precision
If manual calibration of camera parameters is performed to improve accuracy, then measurement precision improves, but the time and effort required for calibration increases
Solution Approach 1:
The system performs self-calibration by automatically capturing test images of a calibration pattern, detecting features in the images, and computing actual camera parameters without requiring manual intervention. The computer automatically determines focal length, principal point, and baseline from the captured images, eliminating time-consuming manual calibration while achieving high measurement precision.
Solution Approach 2:
The system changes camera parameters dynamically during the calibration process by adjusting focal length, principal point, and baseline based on the determined actual settings. This automatic parameter adjustment optimizes measurement precision while minimizing the time required, as the computer rapidly computes and applies the corrected parameters without manual intervention.
3Manufacturing precision
If the physical stereoscopic camera rig is physically adjusted to correct calibration errors, then manufacturing precision improves, but the risk of introducing new errors and the complexity of adjustment increases
Solution Approach 1:
The system uses a virtual copy of the camera rig in software rather than physically adjusting the expensive stereoscopic camera equipment. The virtual cameras can be precisely positioned and configured using the determined actual settings without risking mechanical damage or introducing new alignment errors. This digital approach maintains manufacturing precision while dramatically improving ease of operation.
Solution Approach 2:
The system replaces mechanical adjustment of camera parameters with software-based control of virtual camera parameters. Instead of physically moving camera components or adjusting mechanical mounts, the system uses computer software to control focal length, principal point, and baseline, eliminating mechanical errors and simplifying operation.
Data Source
AI summary
A computer-implemented method for adjusting stereo images includes receiving a video sequence associated with a recorded setting of a stereoscopic camera, the video sequence comprising pairs of left and right images. The method includes processing the pairs of left and right images to reduce influence of a discrepancy between the recorded setting and an actual configuration of the stereoscopic camera.