• Title/Summary/Keyword: 3D Model Registration

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A Study on the Development Cadastral Model for 3D Cadastre Registration (3차원 지적등록을 위한 모형개발에 관한 연구)

  • Kim Kam Lge;Lim Gun Hyuk;Park Se Jin;Hwang Bo Sang Won
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.23 no.2
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    • pp.129-136
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    • 2005
  • The usage of the land is enlarged with above-surface space and sub-surface space as well as surface, as the concern about the land use is increased because there are many factors like the recent development of the science technique, Rapid increase of the population, Rapid city's diffusion, industrialization. According to the cubicalness of the land usage such as these, We have the limit to register the right-objects in space like various buildings and facilities by present registration method of 2D cadastre registration because of 2D registration object. Thus, it is necessary for plan to register an ownership, other privilege relations and the physical objects like various buildings and facilities established on both surface and space. There are two steps in this study. First step is the necessity of 3D cadastre registration and object analysis of cadastre registration, second step is 3D cadastre division and its visualization. Finally, this study will present the realizable model for a 3D cadastre registration.

Registration-free 3D Point Cloud Data Acquisition Technique for as-is BIM Generation Using Rotating Flat Mirrors

  • Li, Fangxin;Kim, Min-Koo;Li, Heng
    • International conference on construction engineering and project management
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    • 2020.12a
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    • pp.3-12
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    • 2020
  • Nowadays, as-is BIM generation has been popularly adopted in the architecture, engineering, construction and facility management (AEC/FM) industries. In order to generate a 3D as-is BIM of a structural component, current methods require a registration process that merges different sets of point cloud data obtained from multiple locations, which is time-consuming and registration error-prone. To tackle this limitation, this study proposes a registration-free 3D point cloud data acquisition technique for as-is BIM generation. In this study, small-size mirrors that rotate in both horizontal and vertical direction are used to enable the registration-free data acquisition technique. First, a geometric model that defines the relationship among the mirrors, the laser scanner and the target component is developed. Second, determinations of optimal laser scanner location and mirror location are performed based on the developed geometrical model. To validate the proposed registration-free as-is BIM generation technique, simulation tests are conducted on key construction components including a PC slab and a structural wall. The result demonstrates that the registration-free point cloud data acquisition technique can be applicable in various construction elements including PC elements and structural components for as-is BIM generation.

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Region-Based 3D Image Registration Technique for TKR (전슬관절치환술을 위한 3차원 영역기반 영상정합 기술)

  • Key, J.H.;Seo, D.C.;Park, H.S.;Youn, I.C.;Lee, M.K.;Yoo, S.K.;Choi, K.W.
    • Journal of Biomedical Engineering Research
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    • v.27 no.6
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    • pp.392-401
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    • 2006
  • Image Guided Surgery (IGS) system which has variously tried in medical engineering fields is able to give a surgeon objective information of operation process like decision making and surgical planning. This information is displayed through 3D images which are acquired from image modalities like CT and MRI for pre-operation. The technique of image registration is necessary to construct IGS system. Image registration means that 3D model and the object operated by a surgeon are matched on the common frame. Major techniques of registration in IGS system have been used by recognizing fiducial markers placed on the object. However, this method has been criticized due to additional trauma, its invasive protocol inserting fiducial markers in patient's bone and generating noise data when 2D slice images are acquired by image modality because many markers are made of metal. Therefore, this paper developed shape-based registration technique to improve the limitation of fiducial marker based IGS system. Iterative Closest Points (ICP) algorithm was used to match corresponding points and quaternion based rotation and translation transformation using closed form solution applied to find the optimized cost function of transformation. we assumed that this algorithm were used in Total Knee replacement (TKR) operation. Accordingly, we have developed region-based 3D registration technique based on anatomical landmarks and this registration algorithm was evaluated in a femur model. It was found that region-based algorithm can improve the accuracy in 3D registration.

Region-based ICP algorithm in TKR operation (인공무릎관절 수술에서의 영역기반 ICP 알고리즘)

  • Key Jae-Hong;Lee Moon-Kyu;Lee Chang-Yang;Kim Dong-M.;Yoo Sun-K.;Choi Kui-Won
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2006.05a
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    • pp.185-186
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    • 2006
  • Image Guided Surgery(IGS) system has been developed to provide exquisite and objective information to surgeons for surgical operation process. It is necessary that registration technique is important to match between 3D image model reconstructed from image modalities and the object operated by surgeon. Majority techniques of registration in IGS system have been used by recognizing fiducial markers placed on the object. However, this method has been criticized due to its invasive protocol inserting fiducial markers in patient's bone. Therefore, shape-based registration technique using geometric characteristics of the object has been invested to improve the limitation of IGS system. During Total Knee Replacement(TKR) operation, it is challenge to register with high accuracy by using shape-based registration because the area to acquire sample data from knee is limited. We have developed region-based 3D registration technique based on anatomical landmarks on the object and this registration algorithm was evaluated in femur model. It was found that region-based algorithm can improve the accuracy in 3D registration. We expect that this technique can efficiently improve the IGS system.

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Development of 2D-3D Image Registration Techniques for Corrective Osteotomy for Lower Limbs (하지기형 교정 수술을 위한 2D-3D 영상 정합기술)

  • Rha, In Chan;Bong, Jae Hwan;Park, Shin Suk
    • Journal of the Korean Society for Precision Engineering
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    • v.30 no.9
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    • pp.991-999
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    • 2013
  • Lower limbs deformity is a congenital disease and can also be occurred by an acquired factor. This paper suggests a new technique for surgical planning of Corrective Osteotomy for Lower Limbs (COLL) using 2D-3D medical image registration. Converting to a 3D modeling data of lower limb based on CT (computed tomography) scan, and divide it into femur, tibia and fibula; which composing the lower limb. By rearranging the model based on the biplane 2D images of X-ray data, a 3D upright bone structure was acquired. There are two ways to array the 3D data on the 2D image: Intensity-based registration and feature-based registration. Even though registering Intensity-based method takes more time, this method will provide more precise results, and will improve the accuracy of surgical planning.

Automated Feature-Based Registration for Reverse Engineering of Human Models

  • Jun, Yong-Tae;Choi, Kui-Won
    • Journal of Mechanical Science and Technology
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    • v.19 no.12
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    • pp.2213-2223
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    • 2005
  • In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

Accuracy Evaluation by Point Cloud Data Registration Method (점군데이터 정합 방법에 따른 정확도 평가)

  • Park, Joon Kyu;Um, Dae Yong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.1
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    • pp.35-41
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    • 2020
  • 3D laser scanners are an effective way to quickly acquire a large amount of data about an object. Recently, it is used in various fields such as surveying, displacement measurement, 3D data generation of objects, construction of indoor spatial information, and BIM(Building Information Model). In order to utilize the point cloud data acquired through the 3D laser scanner, it is necessary to make the data acquired from many stations through a matching process into one data with a unified coordinate system. However, analytical researches on the accuracy of point cloud data according to the registration method are insufficient. In this study, we tried to analyze the accuracy of registration method of point cloud data acquired through 3D laser scanner. The point cloud data of the study area was acquired by 3D laser scanner, the point cloud data was registered by the ICP(Iterative Closest Point) method and the shape registration method through the data processing, and the accuracy was analyzed by comparing with the total station survey results. As a result of the accuracy evaluation, the ICP and the shape registration method showed 0.002m~0.005m and 0.002m~0.009m difference with the total station performance, respectively, and each registration method showed a deviation of less than 0.01m. Each registration method showed less than 0.01m of variation in the experimental results, which satisfies the 1: 1,000 digital accuracy and it is suggested that the registration of point cloud data using ICP and shape matching can be utilized for constructing spatial information. In the future, matching of point cloud data by shape registration method will contribute to productivity improvement by reducing target installation in the process of building spatial information using 3D laser scanner.

Registration of the 3D Range Data Using the Curvature Value (곡률 정보를 이용한 3차원 거리 데이터 정합)

  • Kim, Sang-Hoon;Kim, Tae-Eun
    • Convergence Security Journal
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    • v.8 no.4
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    • pp.161-166
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    • 2008
  • This paper proposes a new approach to align 3D data sets by using curvatures of feature surface. We use the Gaussian curvatures and the covariance matrix which imply the physical characteristics of the model to achieve registration of unaligned 3D data sets. First, the physical characteristics of local area are obtained by the Gaussian curvature. And the camera position of 3D range finder system is calculated from by using the projection matrix between 3D data set and 2D image. Then, the physical characteristics of whole area are obtained by the covariance matrix of the model. The corresponding points can be found in the overlapping region with the cross-projection method and it concentrates by removed points of self-occlusion. By the repeatedly the process discussed above, we finally find corrected points of overlapping region and get the optimized registration result.

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Markerless Image-to-Patient Registration Using Stereo Vision : Comparison of Registration Accuracy by Feature Selection Method and Location of Stereo Bision System (스테레오 비전을 이용한 마커리스 정합 : 특징점 추출 방법과 스테레오 비전의 위치에 따른 정합 정확도 평가)

  • Joo, Subin;Mun, Joung-Hwan;Shin, Ki-Young
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.1
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    • pp.118-125
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    • 2016
  • This study evaluates the performance of image to patient registration algorithm by using stereo vision and CT image for facial region surgical navigation. For the process of image to patient registration, feature extraction and 3D coordinate calculation are conducted, and then 3D CT image to 3D coordinate registration is conducted. Of the five combinations that can be generated by using three facial feature extraction methods and three registration methods on stereo vision image, this study evaluates the one with the highest registration accuracy. In addition, image to patient registration accuracy was compared by changing the facial rotation angle. As a result of the experiment, it turned out that when the facial rotation angle is within 20 degrees, registration using Active Appearance Model and Pseudo Inverse Matching has the highest accuracy, and when the facial rotation angle is over 20 degrees, registration using Speeded Up Robust Features and Iterative Closest Point has the highest accuracy. These results indicate that, Active Appearance Model and Pseudo Inverse Matching methods should be used in order to reduce registration error when the facial rotation angle is within 20 degrees, and Speeded Up Robust Features and Iterative Closest Point methods should be used when the facial rotation angle is over 20 degrees.

A Study on the Image-Based 3D Modeling Using Calibrated Stereo Camera (스테레오 보정 카메라를 이용한 영상 기반 3차원 모델링에 관한 연구)

  • 김효성;남기곤;주재흠;이철헌;설성욱
    • Journal of the Institute of Convergence Signal Processing
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    • v.4 no.3
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    • pp.27-33
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    • 2003
  • The image-based 3D modeling is the technique of generating a 3D graphic model from images acquired using cameras. It is being researched as an alternative technique for the expensive 3D scanner. In this paper, we propose the image-based, 3D modeling system using calibrated stereo cameras. The proposed algorithm for rendering, 3D model consists of three steps, camera calibration, 3D reconstruction, and 3D registration step. In the camera calibration step, we estimate the camera matrix for the image aquisition camera. In the 3D reconstruction step, we calculate 3D coordinates using triangulation from corresponding points of the stereo image. In the 3D registration step, we estimate the transformation matrix that transforms individually reconstructed 3D coordinates to the reference coordinate to render the single 3D model. As shown the result, we generated relatively accurate 3D model.

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