Camera Calibration Using Depth Data and Virtual Targets
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Solution Overview
Problem
Conventional camera calibration methods require precise and accurate physical targets, which are costly to produce and prone to errors due to incorrect placement, and they rely on two-dimensional image data, limiting their effectiveness in capturing accurate depth information.
Innovation Solution
The use of depth data to calibrate cameras, allowing for the reconstruction of the calibration target's three-dimensional geometry without the need for a precise physical target, utilizing techniques such as averaging and sub-pixel refinement to reduce noise and improve accuracy, enabling camera calibration using a single frame of reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods use precise physical targets, then calibration accuracy is improved, but manufacturing cost and complexity increase
Solution Approach 1:
The patent replaces expensive physical calibration targets with virtual targets generated through depth map processing. The system captures depth information using a depth camera, processes it to create a virtual calibration target, and uses this virtual target for calibration. This copying approach eliminates the need for costly physical targets while maintaining calibration accuracy, as the virtual target preserves the essential geometric features needed for calibration without the manufacturing complexities of physical targets.
Solution Approach 2:
The patent substitutes the mechanical physical target system with a computational depth-based system. Instead of relying on physical objects with precise manufactured features, the system uses depth camera data and image processing algorithms to create and utilize virtual calibration targets. This replacement of mechanical systems with computational methods reduces manufacturing costs while maintaining or improving calibration precision through flexible digital target generation.
2Ease of operation
If conventional calibration methods use physical targets, then calibration can be performed, but operator errors and placement mistakes increase
Solution Approach 1:
The system performs self-calibration by automatically generating virtual calibration targets from depth map data captured by the depth camera. The calibration process does not require manual placement of physical targets by operators; instead, the system autonomously creates the necessary calibration references from environmental depth information. This self-service approach eliminates operator placement errors and increases reliability, as the virtual targets are programmatically generated with precise geometric properties.
Solution Approach 2:
The patent introduces depth map processing as an intermediary between the camera system and calibration targets. Rather than directly using physical targets that require manual handling, the system uses depth information as an intermediate representation to generate virtual targets. This intermediary layer automatically translates raw depth data into calibrated target representations, eliminating the need for manual target placement and reducing operator errors.
3Device complexity
If conventional calibration uses two-dimensional image data, then calibration process is simple, but depth information accuracy is limited
Solution Approach 1:
The patent transitions from two-dimensional image-based calibration to three-dimensional depth-based calibration. By utilizing depth camera data to create virtual targets with full 3D geometric information, the system achieves accurate depth measurement while maintaining calibration simplicity. The depth dimension provides precise spatial information that 2D images cannot capture, enabling accurate depth calibration without significantly increasing process complexity.
Solution Approach 2:
The system changes the fundamental parameter used for calibration from 2D image coordinates to 3D depth coordinates. By processing depth map data to generate virtual targets with accurate three-dimensional geometry, the system improves depth information accuracy. The calibration process operates in 3D space using depth values, allowing precise measurement of depth parameters while keeping the overall approach conceptually similar to traditional 2D calibration methods.
Data Source
AI summary
An example system disclosed herein includes a camera to: capture a first image set of a first view of an object when the camera is in a first position, the first image set including first infrared data and first RGB data; capture a second image set of a second view of the object when the camera is in a second position, the second image set including second infrared data and second RGB data; and capture a third image set of a third view of the object when the camera is in a third position, the third image set including third infrared data and third RGB data. The example system also includes at least one processor to: identify a plurality of features among the first view, the second view, and the third view; align the second view relative to the first view by using one or more of the features; align the third view relative to the first view by using one or more of the features; determine first three-dimensional data from the first infrared data; determine second three-dimensional data from the second infrared data; determine third three-dimensional data from the third infrared data; average the first three-dimensional data, the second three-dimensional data, and the third three-dimensional data; create a calibration model based on the average; determine a refined view based on the calibration model; and compute a three dimensional to two dimensional projection based on the refined view.