Automated Camera Array Calibration Using Hybrid Depth and RGB Data
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Solution Overview
Problem
Current camera array calibration techniques for creating three-dimensional spatial video are complex, require expert intervention, and are prone to errors due to small sensor movements and synchronization issues, making them unsuitable for commercially viable systems.
Innovation Solution
An automated camera array calibration technique that uses hybrid capture devices capable of generating RGB and depth data to automatically determine camera geometry, employing methods like Iterative Closest Point (ICP) for rough calibration and refining with RGB feature matching, allowing for intrinsic and extrinsic parameter calibration without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration techniques are used, then calibration accuracy can be achieved, but the process becomes complex and requires expert intervention
Solution Approach 1:
The system performs self-calibration by automatically processing depth maps and RGB images from multiple cameras to compute intrinsic and extrinsic parameters without requiring manual intervention or expert knowledge. The automated algorithm processes the captured data to determine camera geometry, eliminating the need for manual calibration procedures while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computational approach. Instead of physically adjusting cameras or using manual measurement tools, the system uses image processing algorithms that automatically compute calibration parameters from captured images and depth maps, substituting mechanical operations with digital processing.
2Reliability
If traditional calibration methods are used, then calibration can be performed, but errors occur due to small sensor movements and synchronization issues
Solution Approach 1:
The system performs preliminary calibration using depth maps before refining with RGB images. This two-stage approach first establishes a rough calibration based on depth data, then refines it using RGB feature matching. The preliminary depth-based calibration provides a stable foundation that reduces the impact of subsequent sensor movements and synchronization issues when processing RGB data.
Solution Approach 2:
The calibration process incorporates feedback mechanisms where the system continuously refines calibration parameters by comparing depth map features with RGB image features. The algorithm adjusts calibration parameters based on the consistency between different data types, allowing the system to correct errors and improve precision through iterative refinement.
3Ease of operation
If automated calibration is implemented, then expert intervention is reduced, but the system must handle complex data processing
Solution Approach 1:
The calibration process is segmented into distinct stages: first processing depth maps to obtain rough calibration parameters, then processing RGB images to refine the calibration. This segmentation allows the complex data processing to be broken down into manageable steps, each handling specific aspects of calibration data, thereby reducing the operational burden while maintaining automation.
Solution Approach 2:
The system uses depth maps as an intermediary between the captured images and the final calibration parameters. Depth maps serve as a intermediate representation that simplifies the initial calibration process, providing a foundation that eases the subsequent RGB-based refinement. This intermediary approach makes the overall complex processing more manageable and automated.
Data Source
AI summary
The automated camera array calibration technique described herein pertains to a technique for automating camera array calibration. The technique can leverage corresponding depth and single or multi-spectral intensity data (e.g., RGB (Red Green Blue) data) captured by hybrid capture devices to automatically determine camera geometry. In one embodiment it does this by finding common features in the depth maps between two hybrid capture devices and derives a rough extrinsic calibration based on shared depth map features. It then uses the intensity (e.g., RGB) data corresponding to the depth maps and uses the features of the intensity (e.g., RGB) data to refine the rough extrinsic calibration.