Abstract: An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

Abstract: An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

Abstract: An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus for controlling an augmented reality (AR) apparatus are provided. The method includes acquiring a video, detecting a human body from the acquired video, performing an action prediction with regard to the detected human body, and controlling the AR apparatus based on a result of the action prediction and a mapping relationship between human body actions and AR functions.

Abstract: Provided is a method and apparatus for aligning a three-dimensional (3D) model. The 3D model alignment method includes acquiring, by a processor, at least one two-dimensional (2D) image including an object, detecting, by the processor, a feature point of the object in the at least one 2D input image using a neural network, estimating, by the processor, a 3D pose of the object in the at least one 2D input image using the neural network, retrieving, by the processor, a target 3D model based on the estimated 3D pose, and aligning, by the processor, the target 3D model and the object based on the feature point.

Abstract: 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.

Abstract: An apparatus for recommending a customer item identifies a purchase tendency of a customer based on an image, determines a recommended item for the customer by selecting a purchase tendency model corresponding to the purchase tendency, and provides information associated with the determined recommended item.

Abstract: An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

Abstract: Disclosed are a pose estimation methods and apparatuses of displaying a virtual object using an estimated pose. The pose estimation method includes receiving an input image and estimating pose information of an object from the input image based on local information of the object.

Abstract: Disclosed is an object pose estimating method and apparatus. The pose estimating method includes acquiring a two-dimensional (2D) image corresponding to an object, extracting a global visual feature and a local geometric feature of the object in the 2D image, and estimating a three-dimensional (3D) pose of the object based on the global visual feature and the local geometric feature.

Abstract: A method and apparatus for controlling an augmented reality (AR) apparatus are provided. The method includes acquiring a video, detecting a human body from the acquired video, performing an action prediction with regard to the detected human body, and controlling the AR apparatus based on a result of the action prediction and a mapping relationship between human body actions and AR functions.

Abstract: The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization.

Abstract: Provided is a method and apparatus for aligning a three-dimensional (3D) model. The 3D model alignment method includes acquiring, by a processor, at least one two-dimensional (2D) image including an object, detecting, by the processor, a feature point of the object in the at least one 2D input image using a neural network, estimating, by the processor, a 3D pose of the object in the at least one 2D input image using the neural network, retrieving, by the processor, a target 3D model based on the estimated 3D pose, and aligning, by the processor, the target 3D model and the object based on the feature point.

Abstract: Disclosed is an object pose estimating method and apparatus. The pose estimating method includes acquiring a two-dimensional (2D) image corresponding to an object, extracting a global visual feature and a local geometric feature of the object in the 2D image, and estimating a three-dimensional (3D) pose of the object based on the global visual feature and the local geometric feature.

Abstract: Disclosed are a pose estimation methods and apparatuses of displaying a virtual object using an estimated pose. The pose estimation method includes receiving an input image and estimating pose information of an object from the input image based on local information of the object.

Abstract: An apparatus for recommending a customer item identifies a purchase tendency of a customer based on an image, determines a recommended item for the customer by selecting a purchase tendency model corresponding to the purchase tendency, and provides information associated with the determined recommended item.

Abstract: The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization.