For instance, given an image of a scene suffering from projective distortion, metric rectification can be used to get the frontoparallel view of it, which leads to a number of practical and smart applications involving projector display.. Combining Convolutional Neural Network and Photometric Refinement for Accurate Homography Estimation Article Full-text available Aug 2019 Lai Kang Yingmei Wei Yuxiang Xie Yanming Guo Homography estimation refers to the problem of computing a 33 matrix which transfers image points between two images of a planar scene or two images captured from the same location.In this paper, we show that based on four coplanar correspondences of two externally uncalibrated cameras, 3D reconstruction can be achieved in Euclidean space with only one uniform scale factor and up to two real solutions.
It is shown that this scale factor is the physical distance from the camera center to the plane formed by the four points in 3D space. Consequently, if this distance is known a priori, then the 3D structure can be completely determined. In order to disambiguate the two solutions, a third view is required in general to give a unique solution. In practice, since the real data are always corrupted with noise, more coplanar correspondences are used and a least squares solution is applied to obtain the estimation of the homography matrix. Experimental results on both simulated and real data show that this reconstruction algorithm works r. Figures - uploaded by Allen Hanson Author content All figure content in this area was uploaded by Allen Hanson Content may be subject to copyright. 3D Reconstruction Free Public FullDiscover the worlds research 17 million members 135 million publications 700k research projects Join for free Public Full-text 1 Content uploaded by Allen Hanson Author content All content in this area was uploaded by Allen Hanson on Apr 19, 2013 Content may be subject to copyright. The method based on the fundamental matrix is not robust to planarity either, but it manages to give at least 50 of valid solutions. It is known that for a perspective camera model observing a planar scene the homography between images should be used to compute the pose Zha96, so we also included the results with this perspective method.. Advances on Pose Estimation and 3D Resconstruction of 2 and 3-View Scenes Thesis Dec 2018 Laura Fernandez julia The study of cameras and images has been a prominent subject since the beginning of computer vision, one of the main focus being the pose estimation and 3D reconstruction. The goal of this thesis is to tackle and study some specific problems and methods of the structure-from-motion pipeline in order to provide improvements in accuracy, broad studies to comprehend the advantages and disadvantages of the state-of-the-art models and useful implementations made available to the public. More specifically, we center our attention to stereo pairs and triplets of images and discuss some of the methods and models able to provide pose estimation and 3D reconstruction of the scene.First, we address the depth estimation task for stereo pairs using block-matching. This approach implicitly assumes that all pixels in the patch have the same depth producing the common artifact known as the foreground fattening effect. ![]() We present the theory of this method and the implementation we have developed with some improvements. We discuss some variants of the method and analyze its parameters and performance.Secondly, we consider the addition of a third view and study the trifocal tensor, which describes the geometric constraints linking the three views. We explore the advantages offered by this operator in the pose estimation task of a triplet of cameras as opposed to computing the relative poses pair by pair using the fundamental matrix. In addition, we present a study and implementation of several parameterizations of the tensor. ![]() We present a method based on the matrix factorization due to Tomasi and Kanade that relies on the orthographic projection. This method can be used in configurations where other methods fail, in particular, when using cameras with long focal length lenses. The performance of our implementation of this method is compared to that given by the perspective-based methods, we consider that the accuracy achieved and its robustness make it worth considering in any SfM procedure View Show abstract. ![]() One reason that homography estimation has drawn much attention from computer vision community is that homography is ubiquitous in practice and numerous applications (e.g., camera calibration 2 3, metric rectification 4 5, 3D reconstruction 6, image mosaicing 7, object tracking 8, etc.) can benefit from it. For instance, given an image of a scene suffering from projective distortion, metric rectification can be used to get the frontoparallel view of it, which leads to a number of practical and smart applications involving projector display.. Combining Convolutional Neural Network and Photometric Refinement for Accurate Homography Estimation Article Full-text available Aug 2019 Lai Kang Yingmei Wei Yuxiang Xie Yanming Guo Homography estimation refers to the problem of computing a 33 matrix which transfers image points between two images of a planar scene or two images captured from the same location.
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