Samsung Electronics Co., Ltd.

Hyun Sung Chang

Minjung Son

Donghoon Sagong

Wonhee Lee

Kyungboo Jung

Jiyeon Kim

Qiang Wang

Young Hun Sung

Sunghoon Hong

Inwoo Ha

Weiming Li

Hyoseok Hwang

Yang Liu

Hao Wang

Nahyup Kang

Yueying Kao

Dong Kyung Nam

Chao Zhang

Ouk Choi

Seungeon Kim

Hojin Ju

Seokhwan Jang

Lin Ma

Juyong Park

Young Ju Jeong

Heesae Lee

Dongkyung Nam

Jaehwan Pi

Hyun Cheol Jeon

Chui Kang

Min-Jung Song

A processor-implemented method with pose estimation includes: determining depth data by sensing a depth of a target scene comprising planes orthogonal to each other; determining normal vectors corresponding to depth points of the depth data; determining orientation data of a device by comparing the normal vectors to orientation candidates; and determining position data of the device based on distances from the device to the planes.

Method and apparatus with pose estimation

An electronic device and method with gaze estimating are disclosed. The method includes obtaining target information of an image, the image including an eye, obtaining a target feature map representing information on the eye in the image based on the target information, and estimating a gaze for the eye in the image based on the target feature map. The target information includes either attention information on the image, or a distance between pixels in the image, or both.

Device and method with gaze estimating

Provided is a method and apparatus for detecting a planar surface, the method including acquiring, based on a pixelwise disparity of an input image estimated in a first network, a pixelwise plane parameter of the input image, determining a pixelwise segment matching probability of the input image based on a second network trained to perform a segmentation of an image, acquiring a segment-wise plane parameter based on the pixelwise plane parameter and the pixelwise segment matching probability, and detecting a planar surface in the input image based on the segment-wise plane parameter.

Planar surface detection apparatus and method

A method with object pose estimation includes: obtaining an instance segmentation image and a normalized object coordinate space (NOCS) map by processing an input single-frame image using a deep neural network (DNN); obtaining a two-dimensional and three-dimensional (2D-3D) mapping relationship based on the instance segmentation image and the NOCS map; and determining a pose of an object instance in the input single-frame image based on the 2D-3D mapping relationship.

Method and apparatus with object pose estimation

A method with object detection includes obtaining a first point cloud feature based on point cloud data of an image and determining at least one object in the image based on the first point cloud feature.

Method and apparatus with object detection

A processor-implemented method includes: determining albedo data in a canonical space and depth data in the canonical space based on input image data including an object, using one or more neural network-based extraction models; generating deformed albedo data and deformed depth data by applying a target shape deformation value respectively to the albedo data and the depth data; generating resultant shaded data by performing shading based on the deformed depth data and a target illumination value; generating intermediate image data based on the resultant shaded data and the deformed albedo data; and generating reconstructed image data from the intermediate image data and the deformed depth data based on a target pose value.

Method and apparatus with image processing and reconstructed image generation

A three-dimensional (3D) rendering method includes extracting sample points from a 3D scene, acquiring rendering result information for the sample points by rendering the sample points, and generating a rendering result image corresponding to an entire rendering based on the rendering result information for the sample points and feature information of the 3D scene.

Three-dimensional (3D) rendering method and apparatus

A processor-implemented method with object pose estimation includes: determining an image feature corresponding to a point cloud of an input image; determining semantic segmentation information, instance mask information, and keypoint information of an object, based on the image feature; and estimating a pose of the object based on the semantic segmentation information, the instance mask information, and the keypoint information.

A processor-implemented neural network operating method, the operating method comprising obtaining a neural network pre-trained in a source domain and a first style feature of the source domain, extracting a second style feature of a target domain from received input data of the target domain, using the neural network, performing domain adaptation of the input data, by performing style matching of the input data based on the first style feature of the source domain and the second style feature of the target domain, and processing the style-matched input data, using the neural network.

Apparatus and method with neural network implementation of domain adaptation

A method, an apparatus, an electronic device for estimating a pose of an object include determining a confidence of a depth image of an object based on a color image and the depth image of the object, estimating a pose of the object based on a three-dimensional (3D) keypoint in response to the depth image being reliable, and estimating the pose of the object based on a two-dimensional (2D) keypoint in response to the depth image being unreliable.

Method, apparatus, electronic device and storage medium for estimating object pose

An image processing method is provided. The method includes generating appearance information corresponding to a canonical space defined by a cube including a plurality of regular grids in a first input image by implementing a first neural network, generating geometric information corresponding to a standard space in a second input image by implementing a second neural network, deforming the plurality of regular grids based on the generated geometric information, generating standardized image information by applying the appearance information to the plurality of deformed regular grids, and generating an output image based on the standardized image information.

Image processing method and apparatus

A method and apparatus with pose determination are provided. The method includes determining first pose information of a computing apparatus dependent on motion sensor information of motion of the computing apparatus, estimating second pose information of the computing apparatus dependent on feature point position information that is pre-defined for an object, and one or more feature points of the object that are extracted from an image captured by the computing apparatus, determining respective reliability values of the first pose information and the second pose information, and determining a pose of the computing apparatus for augmented reality (AR) content based on the first pose information, the second pose information, and the respective reliability values. AR content may be generated based on the determined pose.

Method and apparatus with augmented reality pose determination

A stereo matching method includes obtaining a first feature map associated with a first view image and a second feature map associated with a second view image using a neural network model-based feature extractor, determining respective matching costs between a reference pixel of the first view image and candidate pixels of the second view image using the first feature map and the second feature map, and determining a pixel corresponding to the reference pixel among the candidate pixels based on the determined matching costs.

Stereo matching method and apparatus, image processing apparatus, and training method therefor

A method and apparatus for generating an image and for training an artificial neural network to generate an image are provided. The method of generating an image, including receiving input data comprising conditional information and image information, generating a synthesized image by applying the input data to an image generation neural network configured to maintain geometric information of the image information and to transform the remaining image information based on the conditional information, and outputting the synthesized image.

Computing method and apparatus with image generation

A method and apparatus with feature embedding is provided. The method includes estimating a depth map for each of plural two-dimensional (2D) input images, transforming the depth maps into three-dimensional (3D) information in a point cloud form based on an aggregation of the depth maps, and generating an embedded feature by applying the 3D information to a machine learning model, where the embedded feature includes information about a 3D shape corresponding to a 2D object in the plural 2D input images.

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Seoul, South Korea

Hyun Sung Chang