Multi-Camera Rig Calibration via 3D Model Error Minimization
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Solution Overview
Problem
Multi-camera systems used in Virtual Reality (VR) often suffer from positional and orientational imperfections due to manufacturing tolerances, leading to image distortion and double vision, which degrade user experience.
Innovation Solution
A camera calibration system that performs extrinsic calibration by modeling objects seen by multiple cameras, comparing them to known surroundings, and adjusting calibration parameters using a gradient descent function to minimize error, ensuring accurate positioning and orientation of cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cameras are assembled in a multi-camera system, then the system can capture images from multiple perspectives for 3D rendering, but manufacturing tolerances cause cameras to be positioned and oriented incorrectly, leading to image distortion and double vision
Solution Approach 1:
The patent applies preliminary action by performing calibration before actual VR content capture. A calibration object with known geometry is captured by all cameras, and 3D models are reconstructed to pre-determine accurate position and orientation parameters. This preliminary calibration step establishes correct spatial relationships before production use, eliminating the need for precise manufacturing assembly.
2Measurement precision
If traditional calibration methods are used, then camera parameters can be adjusted, but the process is time-consuming and may not achieve accurate calibration
Solution Approach 1:
The patent implements feedback by reconstructing 3D models from captured images and comparing them against the known ground truth geometry of the calibration object. Calibration parameters are iteratively adjusted based on the difference between reconstructed and actual measurements, creating a closed-loop feedback system that automatically converges to accurate calibration without manual intervention.
Solution Approach 2:
The patent replaces manual mechanical adjustment of camera parameters with an automated computational system. Instead of physically adjusting camera positions and orientations, the system uses image processing and 3D reconstruction algorithms to calculate and apply calibration parameters automatically, significantly reducing calibration time and improving precision.
3Productivity
If cameras are not properly calibrated, then the system can still capture images, but visual artifacts such as distortion and double vision degrade user experience
Solution Approach 1:
The system performs self-calibration by automatically processing its own captured images to determine calibration parameters. The calibration pipeline uses images captured by the cameras themselves to compute 3D models and extract position and orientation information, allowing the system to self-correct without external intervention or additional specialized equipment.
Data Source
AI summary
A camera calibration system jointly calibrates multiple cameras in a camera rig system. The camera calibration system obtains configuration information about the multiple cameras in the camera rig system, such as position and orientation for each camera relative to other cameras. The camera calibration system estimates calibration parameters (e.g., rotation and translation) for the multiple cameras based on the obtained configuration information. The camera calibration system receives 2D images of a test object captured by the multiple cameras and obtains known information about the test object such as location, size, texture and detailed information of visually distinguishable points of the test object. The camera calibration system then generates a 3D model of the test object based on the received 2D images and the estimated calibration parameters. The generated 3D model is evaluated in comparison with the actual test object to determine a calibration error. The calibration parameters for the cameras are updated to reduce the calibration error for the multiple cameras.