• Title/Summary/Keyword: 3차원 도로 기하정보추출

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Extracting Three-Dimensional Geometric Information of Roads from Integrated Multi-sensor Data using Ground Vehicle Borne System (지상 이동체 기반의 다중 센서 통합 데이터를 활용한 도로의 3차원 기하정보 추출에 관한 연구)

  • Kim, Moon-Gie;Sung, Jung-Gon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.11 no.3
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    • pp.68-79
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    • 2008
  • Ground vehicle borne system which is named RoSSAV(Road Safety Survey and Analysis Vehicle) developed in KICT(Korea Institute of Construction Technology) can collect road geometric data. This system therefore is able to evaluate the road safety and analyze road deficient sections using data collected along the roads. The purpose of this study is to extract road geometric data for 3D road modeling in dangerous road section and The system should be able to quickly provide more accurate data. Various sensors(circular laser scanner, GPS, INS, CCD camera and DMI) are installed in moving object and collect road environment data. Finally, We extract 3d road geometry(center, boundary), road facility and slope using integrated multi-sensor data.

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Development of Stereoscopic image editing tool using Image-based Modeling (영상 기반 모델링 기법을 이용한 입체 영상 저작도구 개발)

  • Han, Sang-Heon;Yun, Chang-Ok;Park, Hyun-Woo;Kim, Jung-Hoon;Lee, Young-Bo;Lee, Dong-Hoon;Yun, Tae-Soo
    • 한국HCI학회:학술대회논문집
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    • 2006.02a
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    • pp.1087-1092
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    • 2006
  • 몰입도가 높은 가시화 기법 중 하나인 입체 영상은 차세대 미디어의 표준으로 최근 크게 주목 받고 있다. 그러나 일반 2차원 영상과는 달리 입체 영상은 3차원의 기하정보가 존재해야만 영상을 생성하는 것이 가능하다. 따라서 3차원의 기하정보가 존재하지 않는 2차원 영상을 이용한 입체 영상의 저작은 매우 어려운 문제이다. 본 논문은 영상 기반 모델링 기법을 활용하여 단안 영상으로부터 입체 영상을 생성하기 위한 입체 영상 저작 도구를 제안한다. 이를 위해 입력된 영상에서 사영 기하 정보를 사용하여 깊이 정보를 추론함으로써 3차원 환경을 구성하는 전역 깊이 정보 추출 방법과 영상 내에 존재하는 사물의 정확한 깊이 정보로 수정하기 위한 부분 깊이 정보 수정 방법을 제안한다. 또한, 추출한 깊이 정보로부터 몰입감이 높은 입체 영상의 시점을 결정하기 위한 대화식 입체 영상 미리 보기 기능을 제안한다. 본 논문에서 제안한 기법은 2차원 영상 저작 도구인 포토샵의 플러그인으로 구현함으로써 범용성을 높였다.

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A Semi-Automatic Building Modeling System Using a Single Satellite Image (단일 위성 영상 기반의 반자동 건물 모델링 시스템)

  • Oh, Seon-Ho;Jang, Kyung-Ho;Jung, Soon-Ki
    • The KIPS Transactions:PartB
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    • v.16B no.6
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    • pp.451-462
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    • 2009
  • The spread of satellite image increases various services using it. Especially, 3D visualization services of the whole earth such as $Google\;Earth^{TM}$ and $Virtual\;Earth^{TM}$ or 3D GIS services for several cities provide realistic geometry information of buildings and terrain of wide areas. These service can be used in the various fields such as urban planning, improvement of roads, entertainment, military simulation and emergency response. The research about extracting the building and terrain information effectively from the high-resolution satellite image is required. In this paper, presents a system for effective extraction of the building model from a single high-resolution satellite image, after examine requirements for building model extraction. The proposed system utilizes geometric features of satellite image and the geometric relationship among the building, the shadow of the building, the positions of the sun and the satellite to minimize user interaction. Finally, after extracting the 3D building, the fact that effective extraction of the model from single high-resolution satellite will be show.

3D Road Modeling using LIDAR Data and a Digital Map (라이다데이터와 수치지도를 이용한 도로의 3차원 모델링)

  • Kim, Seong-Joon;Lee, Im-Pyeong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.26 no.2
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    • pp.165-173
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    • 2008
  • This study aims at generating automatically three dimensional geometric models of roads using LIDAR data and a digital map. The main processes in the proposed method are (1) generating a polygon encompassing a road region using a road layer from the digital map, (2) extracting LIDAR points within the road region using the polygon, (3) organizing the points into surface patches and grouping the patches into surface clusters, (4) searching the road surface clusters and generating the surface model from the points linked to the clusters, (5) refining the boundary using a digital map. By applying the proposed method to real data, we successfully generated the linear and surface information of the roads.

3D Reconstruction using a Moving Planar Mirror (움직이는 평면거울을 이용한 3차원 물체 복원)

  • 장경호;이동훈;정순기
    • Journal of KIISE:Software and Applications
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    • v.31 no.11
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    • pp.1543-1550
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    • 2004
  • Modeling from images is a cost-effective means of obtaining 3D geometric models. These models can be effectively constructed from classical Structure from Motion algorithm. However, it's too difficult to reconstruct whole scenes using SFM method since general sites contain a very complex shapes and brilliant colours. To overcome this difficulty, the current paper proposes a new reconstruction method based on a moving Planar mirror. We devise the mirror posture instead of scene itself as a cue for reconstructing the geometry That implies that the geometric cues are inserted into the scene by compulsion. With this method, we can obtain the geometric details regardless of the scene complexity. For this purpose, we first capture image sequences through the moving mirror containing the interested scene, and then calibrate the camera through the mirror's posture. Since the calibration results are still inaccurate due to the detection error, the camera pose is revised using frame-correspondence of the comer points that are easily obtained using the initial camera posture. Finally, 3D information is computed from a set of calibrated image sequences. We validate our approach with a set of experiments on some complex objects.

Vision-based Camera Localization using DEM and Mountain Image (DEM과 산영상을 이용한 비전기반 카메라 위치인식)

  • Cha Jeong-Hee
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.6 s.38
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    • pp.177-186
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    • 2005
  • In this Paper. we propose vision-based camera localization technique using 3D information which is created by mapping of DEM and mountain image. Typically, image features for localization have drawbacks, it is variable to camera viewpoint and after time information quantify increases . In this paper, we extract invariance features of geometry which is irrelevant to camera viewpoint and estimate camera extrinsic Parameter through accurate corresponding Points matching by Proposed similarity evaluation function and Graham search method we also propose 3D information creation method by using graphic theory and visual clues, The Proposed method has the three following stages; point features invariance vector extraction, 3D information creation, camera extrinsic Parameter estimation. In the experiments, we compare and analyse the proposed method with existing methods to demonstrate the superiority of the proposed methods.

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Classification of 3D Road Objects Using Machine Learning (머신러닝을 이용한 3차원 도로객체의 분류)

  • Hong, Song Pyo;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.6
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    • pp.535-544
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    • 2018
  • Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

Texture Mapping of a Bridge Deck Using UAV Images (무인항공영상을 이용한 교량 상판의 텍스처 매핑)

  • Nguyen, Truong Linh;Han, Dongyeob
    • Journal of Digital Contents Society
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    • v.18 no.6
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    • pp.1041-1047
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    • 2017
  • There are many methods for surveying the status of a road, and the use of unmanned aerial vehicle (UAV) photo is one such method. When the UAV images are too large to be processed and suspected to be redundant, a texture extraction technique is used to transform the data into a reduced set of feature representations. This is an important task in 3D simulation using UAV images because a huge amount of data can be inputted. This paper presents a texture extraction method from UAV images to obtain high-resolution images of bridges. The proposed method is in three steps: firstly, we use the 3D bridge model from the V-World database; secondly, textures are extracted from oriented UAV images; and finally, the extracted textures from each image are blended. The result of our study can be used to update V-World textures to a high-resolution image.

Removing Lighting Reflection under Dark and Rainy Environments based on Stereoscopic Vision (스테레오 영상 기반 야간 및 우천시 조명 반사 제거 기술)

  • Lee, Sang-Woong
    • Journal of KIISE:Software and Applications
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    • v.37 no.2
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    • pp.104-109
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    • 2010
  • The lighting reflection is a common problem in image analysis and causes the many difficulties to extract distinct features in related fields. Furthermore, the problem grows in the rainy night. In this paper, we aim to remove light reflection effects and reconstruct a road surface without lighting reflections in order to extract distinct features. The proposed method utilizes a 3D analysis based on a multiple geometry using captured images, with which we can combine each reflected areas; that is, we can remove lighting reflection effects and reconstruct the surface. At first, the regions of lighting sources and reflected surfaces are extracted by local maxima based on vertically projected intensity-histograms. After that, a fundamental matrix and homography matrix among multiple images are calculated by corresponding points in each image. Finally, we combine each surface by selecting minimum value among multiple images and replace it on a target image. The proposed method can reduces lighting reflection effects and the property on the surface is not lost. While the experimental results with collected data shows plausible performance comparing to the speed, reflection-overlapping areas which can not be reconstructed remain in the result. In order to solve this problem, a new reflection model needs to be constructed.

High-resolution 3D Object Reconstruction using Multiple Cameras (다수의 카메라를 활용한 고해상도 3차원 객체 복원 시스템)

  • Hwang, Sung Soo;Yoo, Jisung;Kim, Hee-Dong;Kim, Sujung;Paeng, Kyunghyun;Kim, Seong Dae
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.10
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    • pp.150-161
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    • 2013
  • This paper presents a new system which produces high resolution 3D contents by capturing multiview images of an object using multiple cameras, and estimating geometric and texture information of the object from the captured images. Even though a variety of multiview image-based 3D reconstruction systems have been proposed, it was difficult to generate high resolution 3D contents because multiview image-based 3D reconstruction requires a large amount of memory and computation. In order to reduce computational complexity and memory size for 3D reconstruction, the proposed system predetermines the regions in input images where an object can exist to extract object boundaries fast. And for fast computation of a visual hull, the system represents silhouettes and 3D-2D projection/back-projection relations by chain codes and 1D homographies, respectively. The geometric data of the reconstructed object is compactly represented by a 3D segment-based data format which is called DoCube, and the 3D object is finally reconstructed after 3D mesh generation and texture mapping are performed. Experimental results show that the proposed system produces 3D object contents of $800{\times}800{\times}800$ resolution with a rate of 2.2 seconds per frame.