786 research outputs found
Leveraging Vision Reconstruction Pipelines for Satellite Imagery
Reconstructing 3D geometry from satellite imagery is an important topic of
research. However, disparities exist between how this 3D reconstruction problem
is handled in the remote sensing context and how multi-view reconstruction
pipelines have been developed in the computer vision community. In this paper,
we explore whether state-of-the-art reconstruction pipelines from the vision
community can be applied to the satellite imagery. Along the way, we address
several challenges adapting vision-based structure from motion and multi-view
stereo methods. We show that vision pipelines can offer competitive speed and
accuracy in the satellite context.Comment: Project Page: https://kai-46.github.io/VisSat
An Atlas for the Pinhole Camera
We introduce an atlas of algebro-geometric objects associated with image
formation in pinhole cameras. The nodes of the atlas are algebraic varieties or
their vanishing ideals related to each other by projection or elimination and
restriction or specialization respectively. This atlas offers a unifying
framework for the study of problems in 3D computer vision. We initiate the
study of the atlas by completely characterizing a part of the atlas stemming
from the triangulation problem. We conclude with several open problems and
generalizations of the atlas.Comment: 47 pages with references and appendices, final versio
Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
Camera calibration is an essential first step in setting up 3D Computer
Vision systems. Commonly used parametric camera models are limited to a few
degrees of freedom and thus often do not optimally fit to complex real lens
distortion. In contrast, generic camera models allow for very accurate
calibration due to their flexibility. Despite this, they have seen little use
in practice. In this paper, we argue that this should change. We propose a
calibration pipeline for generic models that is fully automated, easy to use,
and can act as a drop-in replacement for parametric calibration, with a focus
on accuracy. We compare our results to parametric calibrations. Considering
stereo depth estimation and camera pose estimation as examples, we show that
the calibration error acts as a bias on the results. We thus argue that in
contrast to current common practice, generic models should be preferred over
parametric ones whenever possible. To facilitate this, we released our
calibration pipeline at https://github.com/puzzlepaint/camera_calibration,
making both easy-to-use and accurate camera calibration available to everyone.Comment: 15 pages, 12 figures, accepted to CVPR 2020 as an ora
A Full Scale Camera Calibration Technique with Automatic Model Selection – Extension and Validation
This thesis presents work on the testing and development of a complete camera calibration approach which can be applied to a wide range of cameras equipped with normal, wide-angle, fish-eye, or telephoto lenses. The full scale calibration approach estimates all of the intrinsic and extrinsic parameters. The calibration procedure is simple and does not require prior knowledge of any parameters. The method uses a simple planar calibration pattern. Closed-form estimates for the intrinsic and extrinsic parameters are computed followed by nonlinear optimization. Polynomial functions are used to describe the lens projection instead of the commonly used radial model. Statistical information criteria are used to automatically determine the complexity of the lens distortion model.
In the first stage experiments were performed to verify and compare the performance of the calibration method. Experiments were performed on a wide range of lenses. Synthetic data was used to simulate real data and validate the performance. Synthetic data was also used to validate the performance of the distortion model selection which uses Information Theoretic Criterion (AIC) to automatically select the complexity of the distortion model.
In the second stage work was done to develop an improved calibration procedure which addresses shortcomings of previously developed method. Experiments on the previous method revealed that the estimation of the principal point during calibration was erroneous for lenses with a large focal length. To address this issue the calibration method was modified to include additional methods to accurately estimate the principal point in the initial stages of the calibration procedure. The modified procedure can now be used to calibrate a wide spectrum of imaging systems including telephoto and verifocal lenses.
Survey of current work revealed a vast amount of research concentrating on calibrating only the distortion of the camera. In these methods researchers propose methods to calibrate only the distortion parameters and suggest using other popular methods to find the remaining camera parameters. Using this proposed methodology we apply distortion calibration to our methods to separate the estimation of distortion parameters. We show and compare the results with the original method on a wide range of imaging systems
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