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公开(公告)号：US20210209341A1
公开(公告)日：2021-07-08
申请号：US17211491
申请日：2021-03-24
Inventor： Xiaoqing YE , Xiao TAN , Hao SUN , Hongwu ZHANG
Abstract: The present disclosure provides a three-dimensional (3D) object detection method, a 3D object detection apparatus, an electronic device, and a readable storage medium, belonging to a field of computer vision technologies. Two-dimensional (2D) image parameters and initial 3D image parameters are determined for a target object. Candidate 3D image parameters are determined for the target object based on a disturbance range of 3D parameters and the initial 3D image parameters determined for the target object. Target 3D image parameters are selected for the target object from the candidate 3D image parameters determined for the target object based on the 2D image parameters. A 3D detection result of the target object is determined based on the target 3D image parameters.
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公开(公告)号：US20210335008A1
公开(公告)日：2021-10-28
申请号：US17172883
申请日：2021-02-10
Inventor： Xiaoqing YE , Xiao TAN , Hao SUN , Hongwu ZHANG
Abstract: Embodiments of the present disclosure provide a method and apparatus for processing a video frame, and relates to the field of computer vision technology. The method may include: acquiring a plurality of candidate first-order radial distortion parameters preset for a to-be-processed video frame, and acquiring a specified value of a specified radial distortion parameter; performing radial distortion correction on the to-be-processed video frame to obtain a first initial corrected video frame; selecting a first initial corrected video frame in which a local region except for a center region after distortion correction includes a largest number of straight line segments; and determining a candidate first-order radial distortion parameter corresponding to the selected first initial corrected video frame for use as a target first-order radial distortion parameter of the to-be-processed video frame.
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公开(公告)号：US20220004801A1
公开(公告)日：2022-01-06
申请号：US17480053
申请日：2021-09-20
Inventor： Zhikang ZOU , Xiaoqing YE , Hao SUN
Abstract: The present disclosure provides an image processing method and apparatus, a training method for a neural network and apparatus, a device, and a medium. The implementation is: inputting a source domain image and a target domain image into a matching feature extraction network to extract a matching feature of the source domain image and a matching feature of the target domain image, wherein the matching feature of the source domain image and the matching feature of target domain image are mutually matching features in the source domain image and the target domain image, the source domain image is a simulated image generated through rendering based on object pose parameters, and the target domain image is a real image that is actually shot and applicable to training of object pose estimation; and providing the matching feature of the source domain image for the training of the object pose estimation.
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公开(公告)号：US20210358169A1
公开(公告)日：2021-11-18
申请号：US17339177
申请日：2021-06-04
Inventor： Zhikang ZOU , Xiaoqing YE , Xiao TAN , Hao SUN
Abstract: A method and an apparatus for calibrating an external parameter of a camera are provided. The method may include: acquiring a time-synchronized data set of three-dimensional point clouds and two-dimensional image of a calibration reference object, the two-dimensional image being acquired by a camera with a to-be-calibrated external parameter; establishing a transformation relationship between a point cloud coordinate system and an image coordinate system, the transformation relationship including a transformation parameter; back-projecting the data set of the three-dimensional point clouds onto a plane where the two-dimensional image is located through the transformation relationship to obtain a set of projection points of the three-dimensional point clouds; adjusting the transformation parameter to map the set of the projection points onto the two-dimensional image; and obtaining an external parameter of the camera based on the adjusted transformation parameter and the data set of the three-dimensional point clouds.
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