Automated Camera Calibration via Semantic Segmentation and Geometric Constraints
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Solution Overview
Problem
Image capture devices introduce warp or distortion into image data of 3D scenes, making it difficult to process information about the scene, especially when the device's characteristics are unknown or inconsistent.
Innovation Solution
Automated camera calibration is achieved by identifying features in image data using semantic segmentation and geometric constraints to generate three-dimensional reference points, filtering candidate camera parameter sets to derive reliable camera parameters for processing the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated calibration is performed without prior device characteristics, then adaptability to different devices is improved, but measurement precision of camera parameters deteriorates due to unknown device characteristics
Solution Approach 1:
The system performs self-calibration by automatically detecting features in the captured image and computing camera parameters without requiring external calibration equipment or prior knowledge of device characteristics. The algorithm identifies geometric features, extracts reference points, and solves for camera parameters autonomously, enabling the system to adapt to different devices while maintaining measurement precision through iterative optimization and validation.
2Reliability
If multiple candidate sets of camera parameters are generated, then reliability of final parameters is improved through filtering, but loss of time increases due to processing multiple candidates
Solution Approach 1:
The system generates multiple candidate sets of camera parameters by sampling different feature subsets and applying the calibration algorithm to each. Rather than exhaustively evaluating all possible candidates, the system uses statistical sampling and early termination strategies - stopping the evaluation of a candidate set when it clearly fails validation criteria, thus reducing processing time while maintaining high reliability through multiple independent validation checks.
3Ease of operation
If semantic segmentation is used to identify features, then ease of operation is improved through automated feature detection, but device complexity increases due to machine learning model requirements
Solution Approach 1:
The system introduces an intermediate processing stage where semantic segmentation models pre-identify potential features and generate candidate reference points before the main calibration algorithm executes. This intermediary step simplifies the operation for users by automatically handling complex feature detection, while the calibration algorithm itself remains computationally efficient by working with the pre-processed feature candidates rather than raw image data.
Data Source
AI summary
Aspects of the present disclosure relate to automated camera calibration. In examples, features are identified within image data of a scene that was captured by an image capture device. For instance, semantic segmentation may be used to identify the features within the image data. The identified features may be processed based on one or more geometric constraints to generate three-dimensional reference points within the scene that are associated with two-dimensional locations of the image data. Multiple candidate sets of camera parameters may be generated based on the reference points. Noisy and/or unreliably candidate sets may be omitted, and remaining candidate sets of camera parameters may be used to generate a final set of camera parameters. The final set of camera parameters may be used to derive information associated with the scene from which the image data was captured.