207 research outputs found
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images
Keypoint detection and matching is a fundamental task in many computer vision
problems, from shape reconstruction, to structure from motion, to AR/VR
applications and robotics. It is a well-studied problem with remarkable
successes such as SIFT, and more recent deep learning approaches. While great
robustness is exhibited by these techniques with respect to noise, illumination
variation, and rigid motion transformations, less attention has been placed on
image distortion sensitivity. In this work, we focus on the case when this is
caused by the geometry of the cameras used for image acquisition, and consider
the keypoint detection and matching problem between the hybrid scenario of a
fisheye and a projective image. We build on a state-of-the-art approach and
derive a self-supervised procedure that enables training an interest point
detector and descriptor network. We also collected two new datasets for
additional training and testing in this unexplored scenario, and we demonstrate
that current approaches are suboptimal because they are designed to work in
traditional projective conditions, while the proposed approach turns out to be
the most effective.Comment: CVPR Workshop on Omnidirectional Computer Vision, 202
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal
vanishing points, and the unknown focal length of the camera, leveraging a
prior vertical direction. The direction can come from an Inertial Measurement
Unit that is a standard component of recent consumer devices, e.g.,
smartphones. We provide an exhaustive analysis of minimal line configurations
and derive two new 2-line solvers, one of which does not suffer from
singularities affecting existing solvers. Additionally, we design a new
non-minimal method, running on an arbitrary number of lines, to boost the
performance in local optimization. Combining all solvers in a hybrid robust
estimator, our method achieves increased accuracy even with a rough prior.
Experiments on synthetic and real-world datasets demonstrate the superior
accuracy of our method compared to the state of the art, while having
comparable runtimes. We further demonstrate the applicability of our solvers
for relative rotation estimation. The code is available at
https://github.com/cvg/VP-Estimation-with-Prior-Gravity.Comment: Accepted at ICCV 202
Body-relative navigation guidance using uncalibrated cameras
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-97) and index.The ability to navigate through the world is an essential capability to humans. In a variety of situations, people do not have the time, the opportunity or the capability to learn the layout of the environment before visiting an area. Examples include soldiers in the field entering an unknown building, firefighters responding to an emergency, or a visually impaired person walking through the city. In absence of external source of localization (such as GPS), the system must rely on internal sensing to provide navigation guidance to the user. In order to address real-world situations, the method must provide spatially extended, temporally consistent navigation guidance, through cluttered and dynamic environments. While recent research has largely focused on metric methods based on calibrated cameras, the work presented in this thesis demonstrates a novel approach to navigation using uncalibrated cameras. During the first visit of the environment, the method builds a topological representation of the user's exploration path, which we refer to as the place graph. The method then provides navigation guidance from any place to any other in the explored environment. On one hand, a localization algorithm determines the location of the user in the graph. On the other hand, a rotation guidance algorithm provides a directional cue towards the next graph node in the user's body frame. Our method makes little assumption about the environment except that it contains descriptive visual features. It requires no intrinsic or extrinsic camera calibration, and relies instead on a method that learns the correlation between user rotation and feature correspondence across cameras. We validate our approach using several ground truth datasets. In addition, we show that our approach is capable of guiding a robot equipped with a local obstacle avoidance capability through real, cluttered environments. Finally, we validate our system with nine untrained users through several kilometers of indoor environments.by Olivier Koch.Ph.D
New Results on Triangulation, Polynomial Equation Solving and Their Application in Global Localization
This thesis addresses the problem of global localization from images. The overall goal is to find the location and the direction of a camera given an image taken with the camera relative a 3D world model. In order to solve the problem several subproblems have to be handled. The two main steps for constructing a system for global localization consist of model building and localization. For the model construction phase we give a new method for triangulation that guarantees that the globally optimal position is attained under the assumption of Gaussian noise in the image measurements. A common framework for the triangulation of points, lines and conics is presented. The second contribution of the thesis is in the field of solving systems of polynomial equations. Many problems in geometrical computer vision lead to computing the real roots of a system of polynomial equations, and several such geometry problems appear in the localization problem. The method presented in the thesis gives a significant improvement in the numerics when Gröbner basis methods are applied. Such methods are often plagued by numerical problems, but by using the fact that the complete Gröbner basis is not needed, the numerics can be improved. In the final part of the thesis we present several new minimal, geometric problems that have not been solved previously. These minimal cases make use of both two and three dimensional correspondences at the same time. The solutions to these minimal problems form the basis of a localization system which aims at improving robustness compared to the state of the art
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