Fisheye Camera Calibration via Deep Learning Single Image
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Solution Overview
Problem
Current fisheye lens calibration techniques are inadequate in terms of ease of use and accuracy, particularly for amateur users, as they require multiple images and manual input for feature extraction, and self-calibration methods often produce inaccurate results due to reliance on feature correspondence and textureless regions.
Innovation Solution
A deep learning-based method that uses a single fisheye image to calibrate the camera model and rectify distorted images by employing a unified camera model with pretrained convolutional neural networks to estimate focal length, radial distortion parameters, and principle point, allowing for automatic calibration and projection to an equirectangular image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration target techniques are used to provide accurate calibration results, then measurement precision is improved, but device complexity and ease of operation deteriorate due to requiring multiple images and manual input
Solution Approach 1:
The system performs self-calibration by automatically detecting calibration targets and extracting features without requiring manual user input. The deep learning model autonomously processes the calibration images, extracts keypoint correspondences, and computes camera parameters, eliminating the need for user intervention in feature extraction and calibration computation.
Solution Approach 2:
The patent replaces manual mechanical operations (physically positioning and capturing multiple calibration images at different viewpoints) with an automated digital system using deep learning neural networks that process images to extract calibration information automatically.
2Ease of operation
If self-calibration methods are used to simplify the process, then ease of operation is improved, but measurement precision deteriorates due to reliance on feature correspondence and textureless regions
Solution Approach 1:
The patent introduces a deep learning-based feature extraction system as an intermediary that bridges the gap between simple self-calibration and accurate calibration. This intermediary automatically detects and matches calibration target features, providing both ease of use and high accuracy by reliably identifying characteristic points on calibration targets.
Solution Approach 2:
The system changes the approach to feature extraction by using deep learning neural networks that have been trained to recognize calibration target patterns. This transforms the feature extraction process from traditional computer vision methods to a learned parameter-based approach that achieves both automation and accuracy.
3Measurement precision
If multiple calibration images are captured to improve calibration quality, then measurement precision is improved, but loss of time increases due to requiring a sequence of images at different viewpoints
Solution Approach 1:
The patent segments the calibration process into independent components: calibration target detection, feature extraction, and parameter computation. This segmentation allows the system to process calibration information from a single image rather than requiring multiple images, reducing time while maintaining accuracy.
Solution Approach 2:
The deep learning model performs preliminary feature extraction and calibration computation directly from the single calibration image, eliminating the need for subsequent processing of multiple images at different viewpoints, thus reducing overall calibration time.
Data Source
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AI summary
Techniques related to calibrating fisheye cameras using a single image are discussed. Such techniques include applying a first pretrained convolutional neural network to an input fisheye image to generate camera model parameters excluding a principle point and applying a second pretrained convolutional neural network to the fisheye image and a difference of the fisheye image and a projection of the fisheye image using the camera model parameters to generate the principle point.