How to turn your camera into a perfect pinhole model
- URL: http://arxiv.org/abs/2309.11326v1
- Date: Wed, 20 Sep 2023 13:54:29 GMT
- Title: How to turn your camera into a perfect pinhole model
- Authors: Ivan De Boi, Stuti Pathak, Marina Oliveira, Rudi Penne
- Abstract summary: We propose a novel approach that involves a pre-processing step to remove distortions from images.
Our method does not need to assume any distortion model and can be applied to severely warped images.
This model allows for a serious upgrade of many algorithms and applications.
- Score: 0.38233569758620056
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Camera calibration is a first and fundamental step in various computer vision
applications. Despite being an active field of research, Zhang's method remains
widely used for camera calibration due to its implementation in popular
toolboxes. However, this method initially assumes a pinhole model with
oversimplified distortion models. In this work, we propose a novel approach
that involves a pre-processing step to remove distortions from images by means
of Gaussian processes. Our method does not need to assume any distortion model
and can be applied to severely warped images, even in the case of multiple
distortion sources, e.g., a fisheye image of a curved mirror reflection. The
Gaussian processes capture all distortions and camera imperfections, resulting
in virtual images as though taken by an ideal pinhole camera with square
pixels. Furthermore, this ideal GP-camera only needs one image of a square grid
calibration pattern. This model allows for a serious upgrade of many algorithms
and applications that are designed in a pure projective geometry setting but
with a performance that is very sensitive to nonlinear lens distortions. We
demonstrate the effectiveness of our method by simplifying Zhang's calibration
method, reducing the number of parameters and getting rid of the distortion
parameters and iterative optimization. We validate by means of synthetic data
and real world images. The contributions of this work include the construction
of a virtual ideal pinhole camera using Gaussian processes, a simplified
calibration method and lens distortion removal.
Related papers
- RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation [88.54817424560056]
We propose a distortion vector map (DVM) that measures the degree and direction of local distortion.
By learning the DVM, the model can independently identify local distortions at each pixel without relying on global distortion patterns.
In the pre-training stage, it predicts the distortion vector map and perceives the local distortion features of each pixel.
In the fine-tuning stage, it predicts a pixel-wise flow map for deviated fisheye image rectification.
arXiv Detail & Related papers (2024-06-27T06:38:56Z) - Single-image camera calibration with model-free distortion correction [0.0]
This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor.
The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation.
At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image.
arXiv Detail & Related papers (2024-03-02T16:51:35Z) - An Adaptive Method for Camera Attribution under Complex Radial
Distortion Corrections [77.34726150561087]
In-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution.
Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load.
We propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens.
arXiv Detail & Related papers (2023-02-28T08:44:00Z) - TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive
SubPixel Refinement of AprilTags [23.568127229446965]
Calibrating wide-angle lenses with current state-of-the-art techniques yields poor results due to extreme distortion at the edge.
We present our methodology for accurate wide-angle calibration.
arXiv Detail & Related papers (2022-10-05T18:57:07Z) - Self-Supervised Camera Self-Calibration from Video [34.35533943247917]
We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models.
Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods.
arXiv Detail & Related papers (2021-12-06T19:42:05Z) - Rethinking Generic Camera Models for Deep Single Image Camera
Calibration to Recover Rotation and Fisheye Distortion [8.877834897951578]
We propose a generic camera model that has the potential to address various types of distortion.
Our proposed method outperformed conventional methods on two largescale datasets and images captured by off-the-shelf fisheye cameras.
arXiv Detail & Related papers (2021-11-25T05:58:23Z) - Self-Calibrating Neural Radiance Fields [68.64327335620708]
We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects.
Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions.
arXiv Detail & Related papers (2021-08-31T13:34:28Z) - How to Calibrate Your Event Camera [58.80418612800161]
We propose a generic event camera calibration framework using image reconstruction.
We show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras.
arXiv Detail & Related papers (2021-05-26T07:06:58Z) - Wide-angle Image Rectification: A Survey [86.36118799330802]
wide-angle images contain distortions that violate the assumptions underlying pinhole camera models.
Image rectification, which aims to correct these distortions, can solve these problems.
We present a detailed description and discussion of the camera models used in different approaches.
Next, we review both traditional geometry-based image rectification methods and deep learning-based methods.
arXiv Detail & Related papers (2020-10-30T17:28:40Z) - A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency.
We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution.
Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
arXiv Detail & Related papers (2020-07-21T10:03:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.