Dense Field View Calibration for Manipulated Images
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Solution Overview
Problem
Existing camera calibration techniques struggle to accurately estimate camera parameters for images from uncontrolled sources, which may have undergone editing, cropping, or warping, as they rely on assumptions about the image conforming to a simplified camera model.
Innovation Solution
The introduction of 'view calibration' using dense fields, specifically the vertical vector field (VVF) and latitude dense field (LDF), which are non-parametric, camera-model agnostic, and translation-invariant, allowing for the estimation of how a world spherical coordinate space is projected within an input image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera calibration techniques are used, then camera parameters can be estimated for controlled images, but the estimation accuracy deteriorates when images are cropped or warped
Solution Approach 1:
The patent transforms the camera calibration problem from estimating discrete camera parameters directly to estimating continuous dense fields (vertical vector field and latitude dense field) that represent geometric transformations. This parameter transformation allows the system to handle cropped and warped images by predicting how each pixel should be transformed, rather than relying on global camera parameters that assume intact images.
Solution Approach 2:
The patent moves from traditional 3D-to-2D projection modeling to a dense field representation that operates at the pixel level across the 2D image plane. By representing the transformation as a dense field of vectors and latitude values rather than a single projection model, the system can locally adapt to cropping and warping operations while maintaining overall geometric consistency.
2Adaptability or versatility
If dense field machine learning model is used, then view calibration parameters can be estimated for manipulated images, but computational complexity increases
Solution Approach 1:
The patent divides the complex camera calibration task into two separate dense field estimation problems: vertical vector field estimation and latitude dense field estimation. This segmentation allows each field to be modeled independently with specialized neural network architectures, reducing the overall complexity compared to a single monolithic model while improving adaptability to different types of image manipulations.
3Ease of manufacture
If traditional camera models are assumed, then calibration is simpler, but accuracy suffers for uncontrolled images
Solution Approach 1:
The patent introduces dense fields (vertical vector field and latitude dense field) as intermediary representations between the input image and the final camera parameters. These dense fields act as mediators that capture the geometric transformation in a manipulation-robust manner, allowing the system to bypass the limitations of traditional camera models while still producing accurate camera parameter estimates for uncontrolled images.
Data Source
AI summary
Systems and methods for image dense field based view calibration are provided. In one embodiment, an input image is applied to a dense field machine learning model that generates a vertical vector dense field (VVF) and a latitude dense field (LDF) from the input image. The VVF comprises a vertical vector of a projected vanishing point direction for each of the pixels of the input image. The latitude dense field (LDF) comprises a projected latitude value for the pixels of the input image. A dense field map for the input image comprising the VVF and the LDF can be directly or indirectly used for a variety of image processing manipulations. The VVF and LDF can be optionally used to derive traditional camera calibration parameters from uncontrolled images that have undergone undocumented or unknown manipulations.