Query: Camera Calibration

title: A Flexible New Technique for Camera Calibration
abstract: We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.

title: Camera Calibration and 3D Reconstruction
abstract: This chapter describes the algorithms for calibrating the image acquisition system (normally a single camera or a stereo vision) that are fundamental for detecting metric information (detecting an object's size or determining accurate measurements of object observer distance the pose) of the scene from the images. The various camera calibration methods are presented that determine the relative intrinsic parameters and the extrinsic parameters that define the geometric transformation to pass from the reference system of the world to that of the camera. The epipolar geometry introduced in Chap. 5 is exploited to solve the problem of correspondence of homologous points in a stereo vision system with the cameras calibrated and not. With the epipolar geometry is simplified the search for the homologous points between the stereo images (the correspondence problem) by introducing the Essential matrix (calibrated approach) and the Fundamental matrix (weak calibrated approach) The algorithms for estimating these matrices are also described, known a priori the corresponding points of a calibration platform. This is also accomplished with the image alignment procedure, known as stereo image rectification. Finally, the triangulation procedures for the 3D reconstruction of the geometry of the scene without ambiguity are described, given the 2D projections of the homologous points of the stereo images, given the calibration parameters of the stereo system. If only the intrinsic parameters are known, the 3D geometry of the scene is reconstructed by estimating the extrinsic parameters of the system at less than a non determinable scale factor. If the calibration parameters of the stereo system are not available but only the correspondences between the stereo images are known, the structure of the scene is recovered through an unknown homography transformation.

title: A versatile camera calibration technique for high accuracy 3D machine vision metrology using off the shelf TV cameras and lenses
abstract: A new technique for three dimensional (3D) camera calibration for machine vision metrology using off the shelf TV cameras and lenses is described. The two stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two stage calibration can be done in real time.

title: A Perceptual Measure for Deep Single Image Camera Calibration
abstract: Most current single image camera calibration methods rely on specific image features or user input, and cannot be applied to natural images captured in uncontrolled settings. We propose directly inferring camera calibration parameters from a single image using a deep convolutional neural network. This network is trained using automatically generated samples from a large scale panorama dataset, and considerably outperforms other methods, including recent deep learning based approaches, in terms of standard L2 error. However, we argue that in many cases it is more important to consider how humans perceive errors in camera estimation. To this end, we conduct a large scale human perception study where we ask users to judge the realism of 3D objects composited with and without ground truth camera calibration. Based on this study, we develop a new perceptual measure for camera calibration, and demonstrate that our deep calibration network outperforms other methods on this measure. Finally, we demonstrate the use of our calibration network for a number of applications including virtual object insertion, image retrieval and compositing.

title: LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry
abstract: This paper proposes a targetless and automatic camera LiDAR calibration method. Our approach extends the hand eye calibration framework to 2D 3D calibration. The scaled camera motions are accurately calculated using a sensor fusion odometry method. We also clarify the suitable motions for our calibration method. Whereas other calibrations require the LiDAR reflectance data and an initial extrinsic parameter, the proposed method requires only the three dimensional point cloud and the camera image. The effectiveness of the method is demonstrated in experiments using several sensor configurations in indoor and outdoor scenes. Our method achieved higher accuracy than comparable state of the art methods.

title: Multi camera Calibration

title: LiDAR Camera Calibration using 3D 3D Point correspondences
abstract: With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D 3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR camera calibration forward, we demonstrate how two cameras with no overlapping field of view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open source software in the form of a ROS package, more information about which can be sought here: this https URL

title: Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture
abstract: The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 degC with the original camera configuration to 1.37 degC. The implementation of the Wallis filter increases the number of tie point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.

title: OpenPTrack: Open source multi camera calibration and people tracking for RGB D camera networks
abstract: It allows to track people in big volumes at sensor frame rate and currently supports a heterogeneous set of 3D sensors. In this work, we describe its user friendly calibration procedure, which consists of simple steps with real time feedback that allow to obtain accurate results in estimating the camera poses that are then used for tracking people. On top of a calibration based on moving a checkerboard within the tracking space and on a global optimization of cameras and checkerboards poses, a novel procedure which aligns people detections coming from all sensors in a x y t i m e space is used for refining camera poses. While people detection is executed locally, in the machines connected to each sensor, tracking is performed by a single node which takes into account detections from all over the network. Here we detail how a cascade of algorithms working on depth point clouds and color, infrared and disparity images is used to perform people detection from different types of sensors and in any indoor light condition. We present experiments showing that a considerable improvement can be obtained with the proposed calibration refinement procedure that exploits people detections and we compare Kinect v1, Kinect v2 and Mesa SR4500 performance for people tracking applications. OpenPTrack is based on the Robot Operating System and the Point Cloud Library and has already been adopted in networks composed of up to ten imagers for interactive arts, education, culture and human robot interaction applications.

title: High precision method of binocular camera calibration with a distortion model.
abstract: A high precision camera calibration method for binocular stereo vision system based on a multi view template and alternative bundle adjustment is presented in this paper. The proposed method could be achieved by taking several photos on a specially designed calibration template that has diverse encoded points in different orientations. In this paper, the method utilized the existing algorithm used for monocular camera calibration to obtain the initialization, which involves a camera model, including radial lens distortion and tangential distortion. We created a reference coordinate system based on the left camera coordinate to optimize the intrinsic parameters of left camera through alternative bundle adjustment to obtain optimal values. Then, optimal intrinsic parameters of the right camera can be obtained through alternative bundle adjustment when we create a reference coordinate system based on the right camera coordinate. We also used all intrinsic parameters that were acquired to optimize extrinsic parameters. Thus, the optimal lens distortion parameters and intrinsic and extrinsic parameters were obtained. Synthetic and real data were used to test the method. The simulation results demonstrate that the maximum mean absolute relative calibration errors are about 3.5e 6 and 1.2e 6 for the focal length and the principal point, respectively, under zero mean Gaussian noise with 0.05 pixels standard deviation. The real result shows that the reprojection error of our model is about 0.045 pixels with the relative standard deviation of 1.0e 6 over the intrinsic parameters. The proposed method is convenient, cost efficient, highly precise, and simple to carry out.