A Transformational Approach to Explicit Stereo Camera Calibration for Improved Euclidean Accuracy of Infrastructure 3D Reconstruction

The spatial accuracy of point clouds generated from a mobile stereo camera set is very sensitive to the intrinsic and extrinsic camera parameters (i.e., camera calibration) used in stereo image-based three-dimensional (3D) reconstruction methods. The existing camera calibration algorithms induce a significant amount of error owing to poor estimation accuracy in camera parameters when they are used for large-scale scenes such as mapping civil infrastructure. This leads to higher uncertainties in the location of 3D points, and may result in the failure of the whole reconstruction process. This paper proposes a novel procedure to address this problem. It hypothesizes that a set of multiple calibration parameters created by videotaping a moving calibration pattern along a specific path can increase overall calibration accuracy and ultimately enhance the Euclidean accuracy of the generated point cloud. This is achieved by using conventional camera calibration algorithms to perform separate estimations ...

It is proposed a method for camera calibration that could be used in stereo systems as well as in stereo head navigation in this paper. A pinhole camera model and two-dimensional planar target are considered. An Iterated Extended Kalman Filter (IEKF) is used to estimate camera parameters. The met hod takes the observed feature points of images as the filter input and the estimated value of the intrinsic and extrinsic camera parameters as the filter output. Both computer simulation and real data experiments have been used to test the proposed method, and good results have been obtained. The RMS error of absolute distance between reprojection feature points is about 0.09 pixels in real experiments. The experimental results show IEKF is also a feasible optimization algorithm for on-line camera calibration. Key words: Camera Calibration, Iterated Extended Kalman Filter, planar target 1. INTRODUCTION Camera calibration is a crucial phase in most vision systems and a first step in 3D reconstruction. It has been broadly applied in machine vision, virtual reality, and three-dimensional reconstruction and so on. Generally, in order to obtain higher calibration precision, in trinsic and extrinsic camera para meters are estimated through nonlinear optimization methods with information acquired from images. Starting from the simplest method we could mention the Least Square Error (LSE)

The implicit calibration is always needed for many visual hull reconstruction in a canonical world coordinate. We use a translational planar object for camera calibration instead of the object with known 3D geometry. However, there is an unavoidable inaccuracy in manufacture and assembly of planar object, that leads to the planar object deviate from the space of 3D calibration object. The deviation of planar object causes an error of control point's world coordinate. This error will decrease calibration precision directly. We propose a correction approach by which the accurate camera calibration is achieved. The original information of control point is used to compute an initial set of camera parameters. With these parameters, we calculate a skew-correction coefficient matrix by which the error of information of the control point is corrected. The new information is used to optimize a set of new camera parameters. This process is then repeated until convergence. In many experiments based on real images, the pixel reprojection errors obtained by our method are about 30% lower than those of traditional method. Increased accuracy of camera calibration directly leads to improvement in 3D reconstruction.

Toward Routine Use of 3D Histopathology as a Research Tool

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.

In the picking robot binocular vision systems research, the camera calibration is often an indispensable step and these basements to locate the target of the object and rebuild the three-dimensional construction based on the robot stereo vision for the follow-up study. So, searching for a high accuracy and simple camera calibration algorithm is of great significance and necessary. However, For most of these camera calibration algorithms, it is necessary to establish a reference object, namely the target, in front of the camera at present, but posing the target is very not convenient or almost impossible in some cases. Therefore, a picking robot online calibration algorithm based on the vision scene was proposed by studying the work environment characteristics of the picking robot binocular vision system and the invariant projective geometry. The experimental results showed that this algorithm’s calibration accuracy and precision good meets to the requirement of the robot binocular vision system camera calibration in the complex environment.

Practical structure and motion recovery from two uncalibrated images using [formula omitted] Constrained Adaptive Differential Evolution

An automatic camera calibration scheme that utilizes a coordinate measuring machine (CMM) and a camera calibration algorithm is presented for a multiple-sensor integrated coordinate measurement system. In the proposed calibration scheme, the touch probe tip carried by the CMM is employed to automatically generate high-precision calibration target points for camera calibration and sensor integration. A camera calibration algorithm with analytical formulations is developed to calibrate camera parameters in three stages without nonlinear minimization procedures. Simulations and experiments were performed to verify the proposed camera calibration algorithm. The precision of the automatic camera calibration scheme is also evaluated.

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.

According to the characteristics of fish-eye camera, such as large field of view and super short focal length, the traditional camera calibration algorithm based on the small hole imaging model cannot achieve the calibration. This paper proposed a fish-eye camera calibration optimization based on the traditional Kannala model. Firstly, the camera imaging model and distortion type of the fish-eye camera are studied, and on the basis of the traditional Kannala model, the piecewise polynomial approximation model is established to realize the original model optimization. Then, the intrinsic parameters and distortion coefficients of the camera are obtained according to the traditional Kannala model and the optimization model，and the distortion correction images are obtained by intrinsic parameters and distortion coefficients. Finally, the advantages of this algorithm are quantitatively and qualitatively analyzed by using the re-projection error and the multiview stereo vision 3D reconstruction of the distorted correction image. The results indicate that the camera parameters and distortion coefficients were obtained by calibration to correct the original image and to carry out 3D reconstruction of multi-view stereo vision, and the reverse projection error analysis and 3D reconstruction visualization of the camera check are proved to be effective in the calibration of the optimized model camera.

We describe a novel camera calibration algorithm for square, circle, and ring planar calibration patterns. An iterative refinement approach is proposed that utilizes the parameters obtained from traditional calibration algorithms as initialization to perform undistortion and unprojection of calibration images to a canonical fronto-parallel plane. This canonical plane is then used to localize the calibration pattern control points and recompute the camera parameters in an iterative refinement until convergence. Undistorting and unprojecting the calibration pattern to the canonical plane increases the accuracy of control point localization and consequently of camera calibration. We have conducted an extensive set of experiments with real and synthetic images for the square, circle and ring pattern, and the pixel reprojection errors obtained by our method are about 50% lower than those of the OpenCV Camera Calibration Toolbox. Increased accuracy of camera calibration directly leads to improvements in other applications; we demonstrate recovery of fine object structure for visual hull reconstruction, and recovery of precise epipolar geometry for stereo camera calibration.

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water‐air interface plane (glass) owing to the density difference. Camera calibration‐reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction‐induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a thorough review of: the nature of the refraction‐induced image deformation and its behaviour in underwater object‐space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration‐reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration‐reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double‐plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction.

One of the most serious obstacles to accurate quantification of the underwater motion of a swimmer's body is image deformation caused by refraction. Refraction occurs at the water‐air interface plane (glass) owing to the density difference. Camera calibration‐reconstruction algorithms commonly used in aquatic research do not have the capability to correct this refraction‐induced nonlinear image deformation and produce large reconstruction errors. The aim of this paper is to provide a through review of: the nature of the refraction‐induced image deformation and its behaviour in underwater object‐space plane reconstruction; the intrinsic shortcomings of the Direct Linear Transformation (DLT) method in underwater motion analysis; experimental conditions that interact with refraction; and alternative algorithms and strategies that can be used to improve the calibration‐reconstruction accuracy. Although it is impossible to remove the refraction error completely in conventional camera calibration‐reconstruction methods, it is possible to improve the accuracy to some extent by manipulating experimental conditions or calibration frame characteristics. Alternative algorithms, such as the localized DLT and the double‐plane method are also available for error reduction. The ultimate solution for the refraction problem is to develop underwater camera calibration and reconstruction algorithms that have the capability to correct refraction

Camera calibration is used to establish a mathematical model and solve the parameters of the camera through the correspondence between a series of scene points and pixel points. How to establish this mapping relationship is a key issue that needs to be solved in camera calibration. Various algorithms of calibration have been proposed by domestic and foreign scholars, including traditional visual calibration algorithm, camera self-calibration algorithm, and active-vision-based calibration algorithm. This paper focuses on some of the most widely used camera calibration algorithms and compares them.