Cross-Spectral Camera Calibration Using ML Feature Mapping
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Solution Overview
Problem
Geometric camera calibration is challenging in systems with cameras sensitive to different parts of the light spectrum, particularly when lighting conditions vary, as the visual appearance of objects differs significantly across spectra, making feature matching difficult and requiring specially designed calibration targets.
Innovation Solution
A machine-learning (ML) based approach is used to find matching feature points between visible light and near infrared (NIR) images by selecting candidate features in the NIR image, defining search windows in the RGB image, and using ML models to score and predict pixel positions, trained on data from a well-calibrated multi-camera system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature matching methods are used for cameras sensitive to different spectra, then calibration can be performed, but the visual appearance differences across spectra make feature matching difficult and require specially designed calibration targets
Solution Approach 1:
The patent uses machine learning models to learn the mapping relationship between different spectral domains by training on paired images from multiple cameras. Instead of relying on specially designed calibration targets, the system learns to copy and transfer feature information across spectra through neural network-based feature extraction and matching, enabling accurate correspondence without specialized physical targets
Solution Approach 2:
The patent transforms the calibration problem from physical domain (specialized targets) to data domain (image feature spaces). By changing the representation parameters from physical target patterns to learned feature embeddings in ML models, the system achieves spectrum-invariant feature matching that works across different lighting and spectral conditions without requiring spectrum-specific calibration targets
2Adaptability or versatility
If cameras with different spectral sensitivities are used, then more information can be captured, but the visual appearance of objects differs significantly across spectra making calibration challenging
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer between cameras with different spectral sensitivities. The ML models learn spectral transformation functions that map features from one spectral domain to another, acting as a mediator that reconciles the visual appearance differences. This intermediary learning layer enables accurate feature matching across spectra by translating between spectral representations rather than directly comparing raw images
Solution Approach 2:
The patent elevates the calibration problem from 2D image space to a higher-dimensional feature space through machine learning. By transforming images into learned feature embeddings that capture spectral-invariant properties, the system operates in an enhanced dimensionality where spectral differences are factorized out, enabling accurate matching across different spectral cameras without being constrained by surface-level visual differences
Data Source
Figure 1A
Figure 1B~1D
Figure 1E~1I
AI summary
A method including capturing a first image of a real-world scene by a first camera sensitive to a first spectrum of light, the first camera having a first light source, capturing a second image of the real-world scene by a second camera sensitive to a second spectrum of light, the second camera having a second light source, identifying at least one feature in the first image, identifying, using a machine learning (ML) model, at least one feature in the second image that matches the at least one feature identified in the first image, mapping pixels in the first image and the second image to rays in a three-dimensional (3D) space based on the matched at least one feature, and calibrating the first camera and the second camera based on the mapping.