Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Xiaoqing Ye

Hao Sun

Xiao Tan

Hongwu Zhang

Zhikang Zou

Wei Zhang

Haifeng Wang

Errui Ding

Jizhou Huang

Deguo Xia

Provided are a road network extraction method, a device, and a storage medium, which relate to the technical field of artificial intelligence and, in particular, to the fields of image processing, computer vision, and the like and are specifically applicable to scenarios such as intelligent transportation and a smart city. A specific implementation scheme includes: extracting a first road network of a target region according to user trajectories of the target region; extracting a second road network of the target region according to a satellite aerial image of the target region; and extract a target road network of the target region according to the first road network, the second road network, and the user trajectories. Efficient and accurate road network extraction can be achieved through techniques in embodiments of the present disclosure.

Road network extraction method, device, and storage medium

The present disclosure provides a method of training a depth estimation network, which relates to fields of computer vision, deep learning, and image processing technology. The method includes: performing a depth estimation on an original image by using a depth estimation network, so as to obtain a depth image for the original image; removing a moving object from the original image so as to obtain a preprocessed image for the original image; estimating a pose based on the original image and modifying the pose based on the preprocessed image; and adjusting parameters of the depth estimation network according to the original image, the depth image and the pose modified. The present disclosure further provides an apparatus of training a depth estimation network, a method and apparatus of estimating a depth of an image, an electronic device, and a storage medium.

Method and apparatus of training depth estimation network, and method and apparatus of estimating depth of image

The present disclosure provides an object pose obtaining method, and an electronic device, relates to technology fields of image processing, computer vision, and deep learning. A detailed implementation is: extracting an image block of an object from an image, and generating a local coordinate system corresponding to the image block; obtaining 2D projection key points in an image coordinate system corresponding to a plurality of 3D key points on a 3D model of the object; converting the 2D projection key points into the local coordinate system to generate corresponding 2D prediction key points; obtaining direction vectors between each pixel point in the image block and each 2D prediction key point, and obtaining a 2D target key point corresponding to each 2D predication key point based on the direction vectors; and determining a pose of the object according to the 3D key points and the 2D target key points.

Object pose obtaining method, and electronic device

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.

Method, apparatus, electronic device and computer readable medium for calibrating external parameter of camera

A vehicle information detection method, a method for training a detection model, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: performing a first target detection operation based on an image of a target vehicle, to obtain a first detection result for target information of the target vehicle; performing an error detection operation based on the first detection result, to obtain error information; and performing a second target detection operation based on the first detection result and the error information, to obtain a second detection result for the target information.

Vehicle information detection method, method for training detection model, electronic device and storage medium

The present disclosure provides a target detection and model training method and apparatus, a device and a storage medium, and relates to the field of artificial intelligence, and in particular, to computer vision and deep learning technologies, which may be applied to smart city and intelligent transportation scenarios. The target detection method includes: performing feature extraction processing on an image to obtain image features of a plurality of stages of the image; performing position coding processing on the image to obtain a position code of the image; obtaining detection results of the plurality of stages of a target in the image based on the image features of the plurality of stages and the position code; and obtaining a target detection result based on the detection results of the plurality of stages.

Target detection and model training method and apparatus, device and storage medium

Embodiments of the present disclosure provide a method and apparatus for detecting a target object in an image. The method includes: performing following prediction operations using a pre-trained neural network: detecting a target object in a two-dimensional image to determine a two-dimensional bounding box of the target object; and determining a relative position constraint relationship between the two-dimensional bounding box of the target object and a three-dimensional projection bounding box obtained by projecting a three-dimensional bounding box of the target object into the two-dimensional image; and the method further including: determining the three-dimensional projection bounding box of the target object, based on the two-dimensional bounding box of the target object and the relative position constraint relationship between the two-dimensional bounding box of the target object and the three-dimensional projection bounding box.

Method and apparatus for detecting target object in image

A vehicle information detection method, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: determining a bird's-eye view of a target vehicle based on an image of the target vehicle; performing feature extraction on the image of the target vehicle and the bird's-eye view respectively, to obtain first feature information corresponding to the image of the target vehicle and second feature information corresponding to the bird's-eye view of the target vehicle; and determining three-dimensional information of the target vehicle based on the first feature information and the second feature information. According to embodiments of the disclosure, accurate detection of vehicle information can be realized based on a monocular image.

Vehicle information detection method, electronic device and storage medium

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.

Three-dimensional object detection method, electronic device and readable storage medium

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.

Method and apparatus for processing video frame

Embodiments of the present disclosure disclose a method and apparatus for generating position information, a device and a medium. A specific embodiment of the method includes: acquiring an image and vehicle position information, wherein the image includes a target element; inputting the image into a pre-established depth map generation model to obtain a first depth map, wherein the focal length of sample images of sample data used during the training of the model is a sample focal length; generating a second depth map based on the sample focal length, the first depth map, and an estimated focal length of the image; determining depth information of the target element according to element position information of the target element in the image and the second depth map; and generating position information of the target element based on the vehicle position information and the depth information of the target element.

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Beijing, China

Xiaoqing Ye