7 results about "Binocular disparity" patented technology

A method for calibrating a binocular camera calibration parameter, an apparatus, a computer device, and a medium. The method comprises: acquiring a binocular disparity map and / or a binocular depth map; performing epipolar alignment detection on the binocular disparity map and / or the binocular depth map to obtain an epipolar alignment error; and on the basis of the epipolar alignment error and a first preset threshold, determining a target calibration parameter to be calibrated of a binocular camera. In the solution, in the process of calibrating the binocular camera calibration parameter, the epipolar alignment error is obtained by performing epipolar alignment detection on the binocular disparity map and / or the binocular depth map, and the target calibration parameter of the binocular camera is determined on the basis of the epipolar alignment error, thereby not only simplifying the calculation of the epipolar alignment error but also improving the precision of the epipolar alignment error, thus enhancing the accuracy of calibration of the calibration parameter.

The invention discloses a quantitative evaluation method, system and device of a stereoscopic visual function and a storage medium, and belongs to the technical field of visual function detection and evaluation, the method comprises the steps that interactive response of a user to a visual stimulation image is continuously acquired, and the image comprises a target stimulation object and at least one interference stimulation object; according to the response result, adopting an adaptive algorithm to iteratively update the binocular parallax parameter until the change trend meets a preset convergence condition, and outputting a first binocular parallax parameter; and calculating and outputting a stereoscopic vision sharpness threshold value according to the parameters, so that by implementing the method and the device, real binocular parallax perception and cognitive judgment based on monocular clues can be effectively distinguished, the stereoscopic vision sharpness can be accurately determined, and the accuracy, objectivity and reliability of stereoscopic vision detection are improved.

The invention discloses a binocular camera calibration parameter calibration method and device, computer equipment and a medium. The method comprises the steps of obtaining a binocular disparity map and / or a binocular depth map; performing epipolar alignment detection on the binocular parallax image and / or the binocular depth image to obtain an epipolar alignment error; and determining a target calibration parameter of the binocular camera to be calibrated based on the epipolar alignment error and a first preset threshold. In the calibration process of the calibration parameters of the binocular camera, epipolar alignment detection is carried out on the binocular disparity map and / or the binocular depth map to obtain the epipolar alignment error, and the target calibration parameters of the binocular camera are determined based on the epipolar alignment error, so that the calculation of the epipolar alignment error is simplified, the precision of the epipolar alignment error is improved, and the calibration accuracy of the binocular camera is improved. And the calibration accuracy of the calibration parameters is improved.

The application provides an image processing method, device and equipment, the method comprising: obtaining a first original image and a second original image corresponding to a target object; obtaining a first disparity image by using a deep learning algorithm based on the first original image and the second original image; obtaining a second disparity image by using a target binocular algorithm based on the first original image and the second original image; fusing the first disparity image and the second disparity image to obtain a target disparity image; and generating a depth image corresponding to the target object based on the target disparity image. Through the technical scheme of the application, the disparity image of the deep learning algorithm and the disparity image of the target binocular algorithm are fused to obtain a high-precision noise-free target disparity image for distance measurement, and the advantages of the deep learning algorithm and the target binocular algorithm in binocular disparity estimation are fully utilized.

The invention relates to a vehicle-mounted suspension display device. The in-vehicle floating display device according to one embodiment of the present invention comprises: an image generation device for outputting a projection image; and a mirror that reflects the projection image from the image generation device and outputs a floating image based on the inverted real image. The reflecting mirror comprises a deformation reflecting mirror or a deformation Fresnel concave reflecting mirror. As a result, it is possible to realize a large-screen clear floating image, and to reduce the feeling of fatigue caused by parallax of both eyes.

The invention provides an automatic parallax adjusting system and an automatic parallax adjusting method. The automatic parallax adjusting system comprises a pair of cameras and a controller. The pair of cameras is used for shooting an object to obtain a first image and a second image. The pair of cameras comprises two lenses, and a distance exists between the two lenses. A controller is electrically coupled to the pair of cameras and configured to perform: correcting a first image with a first correction matrix and correcting a second image with a second correction matrix; cutting the first correction image and the second correction image; defining a comfortable range of a binocular focusing distance according to a viewing distance from the stereoscopic display to the eyes of the user; and calculating a corrected binocular parallax according to the comfort range, the first cut image and the second cut image.

The application provides a kind of image rectification method based on binocular disparity, comprising: obtaining the first view image and the second view image of different depth of field of target scene;The first view image and the second view image are adjusted to the same preset size;Determine the relative position attitude of the first view image and the second view image;According to the relative position attitude, the corresponding pixel points in the first view image and the second view image are adjusted to be aligned in horizontal direction;The first view image and the second view image are binocular depth estimation to obtain the first image, and the first view image or the second view image with relatively smaller depth of field is monocular depth estimation to obtain the second image;The first image and the second image are image fusion.The rectification method provided by the application can successfully generate long-range and near view disparity map, and is obviously superior to monocular disparity estimation method in accuracy.