Electronics and Telecommunications Research Institute

Chang-Woo Chu

Bon-Ki Koo

Hyun-Bin Kim

Byoung-Tae Choi

Chang-Joon Park

Ho-Won Kim

Jae-Chul Kim

Hye-Sun Kim

Dong-Wan Ryoo

Jung-Jae Yu

Kyung-Kyu Kang

Man-Hee Lee

Young-Mi Cha

Yun-Ji Ban

Jin-Sung Choi

Hyun Joon Kang

Seong-Jae Lim

Jae-Hean Kim

Il-Kyu Park

Han-Byul Joo

Jea-Chul Kim

Disclosed herein are a method and apparatus for matching 3D terrain information based on aerial images captured at different altitudes. The method includes receiving a high-altitude numerical height model based on a terrain image captured at a specific high altitude; receiving 3D terrain information observed from a low altitude, which is generated based on a terrain image captured at an altitude lower than the specific high altitude; generating a low-altitude numerical height model by converting the 3D terrain information into a numerical model in the same form as the high-altitude numerical height model; measuring the cross-correlation between the high-altitude numerical height model and the low-altitude numerical height model, thereby calculating matching parameters for enabling the low-altitude numerical height model to match the high-altitude numerical height model; and adjusting the geospatial coordinates of the 3D terrain information based on the matching parameters and outputting georeferenced 3D terrain information in the same coordinate system as the high-altitude numerical height model.

Method and apparatus for matching 3-dimensional terrain information using heterogeneous altitude aerial images

Disclosed herein are an apparatus and method for reconstructing a 3D model. The apparatus for reconstructing a 3D model includes an image acquisition unit for acquiring multi-view images by receiving image signals captured by multiple drones using cameras, a geometric calibration unit for estimating motion variables of the drones based on the acquired multi-view images, and a 3D model creation unit for reconstructing a 3D model of a dynamic object from the matched multi-view images using a multi-view stereo method.

Apparatus for reconstructing 3D model and method for using the same

An image processing apparatus and method for calibrating a depth of a depth sensor. The image processing method may include obtaining a depth image of a target object captured by a depth sensor and a color image of the target object captured by a color camera; and calibrating a depth of the depth sensor by calibrating a geometrical relation between a projector and a depth camera, which are included in the depth sensor, based the obtained depth and color images and calculating a correct feature point on an image plane of the depth camera that corresponds to a feature point of an image plane of the projector.

Image processing method and apparatus for calibrating depth of depth sensor

Disclosed herein is an apparatus and method for extracting correspondences between aerial images. The apparatus includes a line extraction unit, a line direction determination unit, a building top area extraction unit, and a correspondence extraction unit. The line extraction unit extracts lines corresponding buildings from aerial images. The line direction determination unit defines the directions of the lines as x, y and z axis directions based on a two-dimensional (2D) coordinate system. The building top area extraction unit rotates lines in the x and y axis directions so that the lines are arranged in parallel with the horizontal and vertical directions of the 2D image, and then extracts building top areas from rectangles. The correspondence extraction unit extracts correspondences between the aerial images by comparing the locations of the building top areas extracted from the aerial images.

Apparatus and method for extracting correspondences between aerial images

Disclosed herein is a 3D urban modeling apparatus and method. The 3D urban modeling apparatus includes a calibration unit for calibrating data about a translation and a rotation of at least one capturing device at a time that input aerial images and terrestrial images were captured. A building model generation unit generates at least one 3D building model based on the aerial images and the terrestrial images to which results of the calibration have been applied. A terrain model generation unit generates a 3D terrain model by converting an input digital elevation model into a 3D mesh. A texture extraction unit extracts textures related to the building model and the terrain model from the aerial images and the terrestrial images. A model matching unit generates a 3D urban model by matching the building model with the terrain model, which are based on the textures, with each other.

Three-dimensional urban modeling apparatus and method

The present invention relates to a method and apparatus for capturing a motion of a dynamic object, and restore appearance information of an object making a dynamic motion and motion information of main joints from multi-viewpoint video images of motion information of a dynamic object such as a human body, making a motion through a motion of a skeletal structure on the basis of the skeletal structure, acquired by using multiple cameras. According to the exemplary embodiments of the present invention, it is possible to restore motion information of the object making a dynamic motion by using only an image sensor for a visible light range and to reproduce a multi-viewpoint image by effectively storing the restored information. Further, it is possible to restore motion information of the dynamic object without attaching a specific marker.

Method and apparatus for capturing motion of dynamic object

Provided is an apparatus for reconstructing a 3D shape of an object by using Helmholtz stereopsis. The apparatus includes: an image capturer for capturing two pairs of images satisfying Helmholtz reciprocity condition; a preprocessor for estimating optical and geometrical characteristics of a camera used for the image capturing; and a geometrical data reconstructor for converting Helmholtz restrictions generated from the respective pairs of the images into partial differential equations on the basis of the estimated optical and geometrical characteristics, and reconstructing 3D surface data of an object with depth discontinuity at a boundary between regions on the basis of the partial differential equations.

Apparatus and method for reconstructing 3D shape of object with depth discontinuity using Helmholtz stereopsis

Provided is an apparatus and method for determining stereo disparity based on two-path dynamic programming and GGCP. The apparatus includes a pre-processing unit for analyzing texture distribution of an input image by using a Laplacian of Gaussian (LOG) filter and dividing the input image into a homogeneous region and a non-homogeneous region; a local matching unit for determining candidate disparities to be included in an each pixel of all pixels; a local post-processing unit for removing candidate disparities in a pixel of low reliability by performing a visibility test betweens candidate disparities in each pixel to improve the reliability of the candidate disparity; and a global optimizing unit for determining a final disparity for candidate disparities in an each pixel by performing a dynamic programming.

Apparatus and method for determining stereo disparity based on two-path dynamic programming and GGCP

Provided is a method for generating a three-dimensional (3D) mesh based on unorganized sparse 3D points to generate a mesh model that displays a 3D surface by using unorganized sparse 3D points extracted from a plurality of two-dimensional image. The 3D mesh generating method based on unorganized sparse 3D points includes the steps of: receiving a plurality of unorganized sparse 3D points, a plurality of two-dimensional (2D) corresponding point information, and images; generating an initial mesh by using the received 2D corresponding information; removing an abnormal face from the initial mesh; checking if unused 2D corresponding point information exists among the received 2D corresponding point information; if unused 2D corresponding point information exists, reorganizing the initial mesh by performing a constrained Delaunay triangulation; and if unused 2D corresponding point information does not exist in the result of the confirmation, generating a final mesh.

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Daejeon, South Korea

Chang-Woo Chu