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3 Line images for the equiangular camera model. The black curve is the image of any line in the focal plane i.e., the plane containing the optical center and perpendicular to the optical axis. It is naturally a circle and corresponds to the hemispheric part of the field of view. The other curves show the images of lines whose interpretation planes (plane spanned by a line and the optical center) form angles of 70, 50, 30, and 10 degrees with the optical axis, respectively. The limiting case of 0 degrees corresponds to lines that are coplanar with the optical axis and whose images are lines going through the distortion center. Although in general, line images are not algebraic curves, the parts within the hemispheric field of view, i.e., within the black circle, can be relatively well approximated by conics.
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This survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-develope...
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Citations
... Camera calibration is the task of estimating the mapping from image points to their corresponding ray directions [33]. Although alternatives exist [31,71], this mapping is typically modeled using a set of intrinsic parameters (or intrinsics for short), which depend on the chosen camera model [51,78,83]. Accurate intrinsics are crucial for many computer vision tasks that aim to recover the 3D geometry of an observed scene, as e.g., in SLAM [14,56,57], Structure-from-Motion (SfM) [5,60,70] and novel-view synthesis [40,55]. ...
We present AnyCalib, a method for calibrating the intrinsic parameters of a camera from a single in-the-wild image, that is agnostic to the camera model. Current methods are predominantly tailored to specific camera models and/or require extrinsic cues, such as the direction of gravity, to be visible in the image. In contrast, we argue that the perspective and distortion cues inherent in images are sufficient for model-agnostic camera calibration. To demonstrate this, we frame the calibration process as the regression of the rays corresponding to each pixel. We show, for the first time, that this intermediate representation allows for a closed-form recovery of the intrinsics for a wide range of camera models, including but not limited to: pinhole, Brown-Conrady and Kannala-Brandt. Our approach also applies to edited -- cropped and stretched -- images. Experimentally, we demonstrate that AnyCalib consistently outperforms alternative methods, including 3D foundation models, despite being trained on orders of magnitude less data. Code is available at https://github.com/javrtg/AnyCalib.
... A crucial task in computer vision [19] is edge detection [20]. An edge is often a sharp change in colour from one pixel value to another, like from black to white. ...
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... The pinhole camera model is a fundamental concept in camera imaging technology and represents the simplest form of a camera [15]. It consists of a tiny hole, or aperture, through which light rays from a 3D scene pass and form an image on a plane on the opposing side of the hole. ...
... Projection of point P onto the image plane using the Pinhole Camera Model[15]. ...
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... To guarantee the comprehensiveness of this undertaking and to facilitate the comprehension of subsequent chapters, a concise overview of the camera models commonly utilized in building camera placement methods is present, along with a discussion of the constraints applicable to individual cameras [37]. To explore thoroughly into the subject matter, it is recommended that readers consult the subsequent references for a comprehensive understanding, as the literature includes discussions on camera parameters and influences, such as the work by Mavrinac and Chen [38] who explored camera network coverage models, while a survey of theoretical models used in computer vision research [39] is provided by Sturm et al. [40]. Construction sites frequently utilize bullet, hemispherical, and omnidirectional cameras for surveillance purposes. ...
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... Today, various technologies for measuring 3D information can be found in the market. The most common ways to contactless estimate the distance between a sensor and a surface, are stereo vision (Cyganek & Siebert, 2011;Ma et al., 2012;Sturm & Ramalingam, 2011), F I G U R E 1 Visualization of the proposed calibration pipeline: (1) A robot with an attached 3D sensor captures multiple recordings of an arbitrary scene by moving the eye-in-hand sensor. (2) The 3D scans transformed to the coordinate frame of the robot's base by using its kinematic model. ...
Both robot and hand‐eye calibration have been object of research for decades. While current approaches manage to precisely and robustly identify the parameters of a robot's kinematic model, they still rely on external devices such as calibration objects, markers and/or external sensors. Instead of trying to fit recorded measurements to a model of a known object, this paper treats robot calibration as an offline SLAM problem, where scanning poses are linked to a fixed point in space via a moving kinematic chain. As such,we enable robot calibration by using nothing but an arbitrary eye‐in‐hand depth sensor.To the authors' best knowledge the presented framework is the first solution to three‐dimensional (3D) sensor‐based robot calibration that does not require external sensors nor reference objects. Our novel approach utilizes a modified version of the IterativeCorresponding Point algorithm to run bundle adjustment on multiple 3D recordings estimating the optimal parameters of the kinematic model. A detailed evaluation of the system is shown on a real robot with various attached 3D sensors. The presented results show that the system reaches precision comparable to a dedicated external tracking system at a fraction of its cost.
... In order to achieve this goal, a typical CV system consists of multiple components and stages that work together to process and interpret visual data [37], [38]. The anatomy of CV can be broken down into key stages such as image acquisition [39], preprocessing [40], feature extraction [41], object recognition [42], tracking [43], and interpretation [44]. Each of these stages plays a critical role in enabling computers to make sense of visual data and extract useful information from it. ...
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... The intrinsic calibration of a sensor is typically completed by determining a number of parameters in some proposed sensor model that aims to represent the physical reality of the involved hardware [1,2]. Often, this strategy implies non-flexible models with unstable parameter values ( [3,4], Chapter 3 in [5]). ...
We propose a new paradigm for modelling and calibrating laser scanners with rotation symmetry, as is the case for lidars or for galvanometric laser systems with one or two rotating mirrors. Instead of bothering about the intrinsic parameters of a physical model, we use the geometric properties of the device to model it as a specific configuration of lines, which can be recovered by a line-data-driven procedure. Compared to universal data-driven methods that train general line models, our algebraic-geometric approach only requires a few measurements. We elaborate the case of a galvanometric laser scanner with two mirrors, that we model as a grid of hyperboloids represented by a grid of 3×3 lines. This provides a new type of look-up table, containing not more than nine elements, lines rather than points, where we replace the approximating interpolation with exact affine combinations of lines. The proposed method is validated in a realistic virtual setting. As a collateral contribution, we present a robust algorithm for fitting ruled surfaces of revolution on noisy line measurements.
... Numerous geometric camera models [5] have been proposed, ranging from specific global models of a dozen parameters to generic local models of thousands of parameters. Traditional geometric camera models are tailored for specific lens types, and expressed by a closed-form function of usually <100 parameters. ...
... The generic B-spline model [19] was enhanced in [6] with a denser grid of control points for the cubic B-spline surface, and it was shown that generic models led to more accurate results than traditional global models in photogrammetric applications. Authors of [5], [20] extensively reviewed existing camera models and established a taxonomy based on several criteria. In this paper, we survey the camera models commonly found in GCC tools and provide their exact formulations for reference (Section III-A). ...
... where the tangential distortion [δ ud , δ vd ] is given in (5). Overall, the intrinsic parameter set is ...
In many camera-based applications, it is necessary to find the geometric relationship between incoming rays and image pixels, i.e., the projection model, through the geometric camera calibration (GCC). Aiming to provide practical calibration guidelines, this work surveys and evaluates the existing GCC tools. The survey covers camera models, calibration targets, and algorithms used in these tools, highlighting their properties and the trends in GCC development. The evaluation compares six target-based GCC tools, namely, BabelCalib, Basalt, Camodocal, Kalibr, the MATLAB calibrator, and the OpenCV-based ROS calibrator, with simulated and real data for cameras of wide-angle and fisheye lenses described by three traditional projection models. These tests reveal the strengths and weaknesses of these camera models, as well as the repeatability of these GCC tools. In view of the survey and evaluation, future research directions of GCC are also discussed.
... The intrinsic calibration of a sensor is typically done by determining a number of parameters in some proposed sensor model that aims to represent the physical reality of the involved hardware Sturm et al (2011); Hartley and Zisserman (2004). Often, this strategy implies non-flexible models with unstable parameter values (Lu and Payandeh (2010); ZC (1993), Chapter 3 in Van Hamme (2016)). ...
We propose a new paradigm for modelling and calibrating laser scanners with rotation symmetry, as is the case for Lidars or for galvanometric laser systems with one or two rotating mirrors. Instead of bothering about the intrinsic parameters of a physical model, we use the geometric properties of the device to model it as a specific configuration of lines, which can be recovered by a line-data-driven procedure. Compared to universal data-driven methods that train general line models, our algebraic-geometric approach only requires a few measurements. For example, a galvanometric laser scanner with two mirrors is modelled as a grid of hyperboloids represented by a grid of 3x3 lines, providing a new type of lookup table: containing not more than 9 elements, lines rather than points, where we replace the approximating interpolation with exact affine combinations of lines. The proposed method is validated in a realistic virtual setting. As a collateral contribution, we present a robust algorithm for fitting ruled surfaces of revolution on noisy line measurements.
... In order to achieve this goal, a typical CV system consists of multiple components and stages that work together to process and interpret visual data [56], [57]. The anatomy of CV can be broken down into key stages such as image acquisition [58], preprocessing [59], feature extraction [60], object recognition [61], tracking [62], and interpretation [63]. Each of these stages plays a critical role in enabling computers to make sense of visual data and extract useful information from it. ...
Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.
