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 processor-implement method with image processing includes: generating a feature map of a first image and detecting a target region in the first image based on the feature map; correcting the detected target region; and processing an object corresponding to the target region, based on the corrected target region.
Abstract:
A method, apparatus, electronic device, and non-transitory computer-readable storage medium with multi-modal feature fusion are provided. The method includes generating three-dimensional (3D) feature information and two-dimensional (2D) feature information based on a color image and a depth image, generating fused feature information by fusing the 3D feature information and the 2D feature information based on an attention mechanism, and generating predicted image information by performing image processing based on the fused feature information.
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:
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 correcting an image error in a naked-eye three-dimensional (3D) display, the method including controlling a flat-panel display displaying a stripe image, calculating a raster parameter of the naked-eye 3D display based on a captured stripe image, and correcting a stereoscopic image displayed on the naked-eye 3D display based on the calculated raster parameter, wherein the naked-eye 3D display includes the flat-panel display and the raster is disclosed.
Abstract:
An eyeglass-less 3D display device, and a device and method that compensate for a displayed margin of error are provided. The display device acquires an image of an integral image display (IID) image captured by a single camera, and compensates for a margin of error which arises due to a discrepancy between the designed position of a micro lens array located on one surface of a 2D panel and the actual position thereof, so as to provide a high-quality 3D image.
Abstract:
Provided are methods and apparatuses for calibrating a three-dimensional (3D) image in a tiled display including a display panel and a plurality of lens arrays. The method includes capturing a plurality of structured light images displayed on the display panel, calibrating a geometric model of the tiled display based on the plurality of structured light images, generating a ray model based on the calibrated geometric model of the tiled display, and rendering an image based on the ray model.
Abstract:
Provided is an apparatus and method for calibrating a multi-layer three-dimensional (3D) display (MLD) that may control a 3D display including a plurality of display layers to display a first image on one of the plurality of display layers, acquire a second image by capturing the first image, calculate a homography between the display layer and an image capturer based on the first image and the second image, and calculate geometric relations of the display layer with respect to the image capturer based on the calculated homography.
Abstract:
A processor-implemented method with image processing includes obtaining point cloud data of a target scene, generating a feature map of the point cloud data by extracting a feature from the point cloud data, for each of a plurality of objects included in the target scene, generating a feature vector indicating the object in the target scene based on the feature map, and reconstructing a panorama of the target scene based on the feature vectors of the objects.