26,542 research outputs found

    Efficient planar camera calibration via automatic image selection

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    This paper details a novel approach to automatically selecting images which improve camera calibration results. An algorithm is presented which identifies calibration images that inherently improve camera parameter estimates based on their geometric configuration or image network geometry. Analysing images in a more intuitive geometric framework allows image networks to be formed based on the relationship between their world to image homographies. Geometrically, it is equivalent to enforcing maximum independence between calibration images, this ensures accuracy and stability when solving the planar calibration equations. A webcam application using the proposed strategy is presented. This demonstrates that careful consideration of image network geometry, which has largely been neglected within the community, can yield more accurate parameter estimates with less images

    TVCalib: Camera Calibration for Sports Field Registration in Soccer

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    Sports field registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar field and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences (e.g., lines, point clouds), based on pixel-level annotations for segments of a known calibration object. The calibration module iteratively minimizes the segment reprojection error induced by the estimated camera parameters. Second, we propose a novel approach for 3D sports field registration from broadcast soccer images. Compared to the typical solution, which subsequently refines an initial estimation, our solution does it in one step. The proposed method is evaluated for sports field registration on two datasets and achieves superior results compared to two state-of-the-art approaches.Comment: Accepted for publication at WACV'2

    A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks

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    From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds

    A Novel Framework for Highlight Reflectance Transformation Imaging

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    We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

    A New Method and Toolbox for Easily Calibrating Omnidirectional Cameras

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    In this paper, we focus on calibration of central omnidirectional cameras, both dioptric and catadioptric. We describe our novel camera model and algorithm and provide a practical Matlab Toolbox, which implements the proposed method. Our method relies on the use of a planar grid that is shown by the user at different unknown positions and orientations. The user is only asked to click on the corner points of the images of this grid. Then, calibration is quickly and automatically performed. In contrast with previous approaches, we do not use any specific model of the omnidirectional sensor. Conversely, we assume that the imaging function can be described by a polynomial approximation whose coefficients are estimated by solving a linear least squares minimization problem followed by a non-linear refinement. The performance of the approach is shown through several calibration experiments on both simulated and real data. The proposed algorithm is implemented as a Matlab Toolbox, which allows any inexpert user to easily calibrate his own camera. The toolbox is completely Open Source and is freely downloadable from the author's Web page

    Extrinsic calibration of a camera-robot system under non-holonomic constraints

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    A novel approach for the extrinsic calibration of a camera-robot system, i.e. the estimation of the pose of the camera with respect to the robot coordinate system, is presented. The method is based on the relative pose of a planar pattern as seen by the camera, estimated along with a predefined set of simple robot motions. This set has been generated so as to exploit the kinematic constraints imposed by the robot architecture and the relative pose between the pattern and the camera coordinate system. The resulting calibration procedure is very simple, making it suitable to be used in a broad range of applications. Experimental evaluations on both synthetic and real data demonstrate the validity of the proposed method.Sociedad Argentina de Informática e Investigación Operativ

    Efficient generic calibration method for general cameras with single centre of projection

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    Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method

    Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

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    Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements
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