• Title/Summary/Keyword: 3D Point cloud

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Automatic Generation of Clustered Solid Building Models Based on Point Cloud (포인트 클라우드 데이터 기반 군집형 솔리드 건물 모델 자동 생성 기법)

  • Kim, Han-gyeol;Hwang, YunHyuk;Rhee, Sooahm
    • Korean Journal of Remote Sensing
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    • v.36 no.6_1
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    • pp.1349-1365
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    • 2020
  • In recent years, in the fields of smart cities and digital twins, research on model generation is increasing due to the advantage of acquiring actual 3D coordinates by using point clouds. In addition, there is an increasing demand for a solid model that can easily modify the shape and texture of the building. In this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. Accordingly, in this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. In the first step, the ground points were removed through the planarity analysis of the point cloud. In the second step, building area was extracted from the ground removed point cloud. In the third step, detailed structural area of the buildings was extracted. In the fourth step, the shape of 3D building models with 3D coordinate information added to the extracted area was created. In the last step, a 3D building solid model was created by giving texture to the building model shape. In order to verify the proposed method, we experimented using point clouds extracted from unmanned aerial vehicle images using commercial software. As a result, 3D building shapes with a position error of about 1m compared to the point cloud was created for all buildings with a certain height or higher. In addition, it was confirmed that 3D models on which texturing was performed having a resolution of less than twice the resolution of the original image was generated.

3D Mesh Creation using 2D Delaunay Triangulation of 3D Point Clouds (2차원 딜로니 삼각화를 이용한 3차원 메시 생성)

  • Choi, Ji-Hoon;Yoon, Jong-Hyun;Park, Jong-Seung
    • Journal of the Korea Computer Graphics Society
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    • v.13 no.4
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    • pp.21-27
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    • 2007
  • The 3D Delaunay triangulation is the most widely used method for the mesh creation via the triangulation of a 3D point cloud. However, the method involves a heavy computational cost and, hence, in many interactive applications, it is not appropriate for surface triangulation. In this paper, we propose an efficient triangulation method to create a surface mesh from a 3D point cloud. We divide a set of object points into multiple subsets and apply the 2D Delaunay triangulation to each subset. A given 3D point cloud is cut into slices with respect to the OBB(Oriented Bounding Box) of the point set. The 2D Delaunay triangulation is applied to each subset producing a partial triangulation. The sum of the partial triangulations constitutes the global mesh. As a postprocessing process, we eliminate false edges introduced in the split steps of the triangulation and improve the results. The proposed method can be effectively applied to various image-based modeling applications.

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A Progressive Rendering Method to Enhance the Resolution of Point Cloud Contents (포인트 클라우드 콘텐츠 해상도 향상을 위한 점진적 렌더링 방법)

  • Lee, Heejea;Yun, Junyoung;Kim, Jongwook;Kim, Chanhee;Park, Jong-Il
    • Journal of Broadcast Engineering
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    • v.26 no.3
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    • pp.258-268
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    • 2021
  • Point cloud content is immersive content that represents real-world objects with three-dimensional (3D) points. In the process of acquiring point cloud data or encoding and decoding point cloud data, the resolution of point cloud content could be degraded. In this paper, we propose a method of progressively enhancing the resolution of sequential point cloud contents through inter-frame registration. To register a point cloud, the iterative closest point (ICP) algorithm is commonly used. Existing ICP algorithms can transform rigid bodies, but there is a disadvantage that transformation is not possible for non-rigid bodies having motion vectors in different directions locally, such as point cloud content. We overcome the limitations of the existing ICP-based method by registering regions with motion vectors in different directions locally between the point cloud content of the current frame and the previous frame. In this manner, the resolution of the point cloud content with geometric movement is enhanced through the process of registering points between frames. We provide four different point cloud content that has been enhanced with our method in the experiment.

Point Cloud Slicing Based on 2D Delaunay Triangulation (2D Delaunay Triangulation을 이용한 점군 절단)

  • Park, Hyeong-Tae;Chang, Min-Ho;Park, Sang-Chul
    • Journal of the Korean Society for Precision Engineering
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    • v.24 no.5
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    • pp.127-134
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    • 2007
  • Presented in the paper is an algorithm to generate a section curve by slicing a point cloud including tens of thousands of points. Although, there have been previous research results on the slicing problem, they are quite sensitive on the density variations of the point cloud, as well as on the local noise in the point cloud. To relive the difficulties, three technological requirements are identified; 1) dominant point sampling, 2) avoiding local vibration, and 3) robustness on the density changes. To satisfy these requirements, we propose a new slicing algorithm which is based on a node-sphere diagram. The algorithm has been implemented and tested with various examples.

Effective Multi-Modal Feature Fusion for 3D Semantic Segmentation with Multi-View Images (멀티-뷰 영상들을 활용하는 3차원 의미적 분할을 위한 효과적인 멀티-모달 특징 융합)

  • Hye-Lim Bae;Incheol Kim
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.12
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    • pp.505-518
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    • 2023
  • 3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

3D Library Platform Construction using Drone Images and its Application to Kangwha Dolmen (드론 촬영 영상을 활용한 3D 라이브러리 플랫폼 구축 및 강화지석묘에의 적용)

  • Kim, Kyoung-Ho;Kim, Min-Jung;Lee, Jeongjin
    • Cartoon and Animation Studies
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    • s.48
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    • pp.199-215
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    • 2017
  • Recently, a drone is used for the general purpose application although the drone was builtfor the military purpose. A drone is actively used for the creation of contents, and an image acquisition. In this paper, we develop a 3D library module platform using 3D mesh model data, which is generated by a drone image and its point cloud. First, a lot of 2D image data are taken by a drone, and a point cloud data is generated from 2D drone images. A 3D mesh data is acquired from point cloud data. Then, we develop a service library platform using a transformed 3D data for multi-purpose uses. Our platform with 3D data can minimize the cost and time of contents creation for special effects during the production of a movie, drama, or documentary. Our platform can contribute the creation of experts for the digital contents production in the field of a realistic media, a special image, and exhibitions.

Object Detection and Post-processing of LNGC CCS Scaffolding System using 3D Point Cloud Based on Deep Learning (딥러닝 기반 LNGC 화물창 스캐닝 점군 데이터의 비계 시스템 객체 탐지 및 후처리)

  • Lee, Dong-Kun;Ji, Seung-Hwan;Park, Bon-Yeong
    • Journal of the Society of Naval Architects of Korea
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    • v.58 no.5
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    • pp.303-313
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    • 2021
  • Recently, quality control of the Liquefied Natural Gas Carrier (LNGC) cargo hold and block-erection interference areas using 3D scanners have been performed, focusing on large shipyards and the international association of classification societies. In this study, as a part of the research on LNGC cargo hold quality management advancement, a study on deep-learning-based scaffolding system 3D point cloud object detection and post-processing were conducted using a LNGC cargo hold 3D point cloud. The scaffolding system point cloud object detection is based on the PointNet deep learning architecture that detects objects using point clouds, achieving 70% prediction accuracy. In addition, the possibility of improving the accuracy of object detection through parameter adjustment is confirmed, and the standard of Intersection over Union (IoU), an index for determining whether the object is the same, is achieved. To avoid the manual post-processing work, the object detection architecture allows automatic task performance and can achieve stable prediction accuracy through supplementation and improvement of learning data. In the future, an improved study will be conducted on not only the flat surface of the LNGC cargo hold but also complex systems such as curved surfaces, and the results are expected to be applicable in process progress automation rate monitoring and ship quality control.

Complete 3D Surface Reconstruction from an Unstructured Point Cloud of Arbitrary Shape by Using a Bounding Voxel Model (경계 복셀 모델을 이용한 임의 형상의 비조직화된 점군으로부터의 3 차원 완전 형상 복원)

  • Li Rixie;Kim Seok-Il
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.8 s.251
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    • pp.906-915
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    • 2006
  • This study concerns an advanced 3D surface reconstruction method that the vertices of surface model can be completely matched to the unstructured point cloud measured from arbitrary complex shapes. The concept of bounding voxel model is introduced to generate the mesh model well-representing the geometrical and topological characteristics of point cloud. In the reconstruction processes, the application of various methodologies such as shrink-wrapping, mesh simplification, local subdivision surface fitting, insertion of is isolated points, mesh optimization and so on, are required. Especially, the effectiveness, rapidity and reliability of the proposed surface reconstruction method are demonstrated by the simulation results for the geometrically and topologically complex shapes like dragon and human mouth.

Direct Finite Element Model Generation using 3 Dimensional Scan Data (3D SCAN DATA 를 이용한 직접유한요소모델 생성)

  • Lee Su-Young;Kim Sung-Jin;Jeong Jae-Young;Park Jong-Sik;Lee Seong-Beom
    • Journal of the Korean Society for Precision Engineering
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    • v.23 no.5 s.182
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    • pp.143-148
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    • 2006
  • It is still very difficult to generate a geometry model and finite element model, which has complex and many free surface, even though 3D CAD solutions are applied. Furthermore, in the medical field, which is a big growth area of recent years, there is no drawing. For these reasons, making a geometry model, which is used in finite element analysis, is very difficult. To resolve these problems and satisfy the requests of the need to create a 3D digital file for an object where none had existed before, new technologies are appeared recently. Among the recent technologies, there is a growing interest in the availability of fast, affordable optical range laser scanning. The development of 3D laser scan technology to obtain 3D point cloud data, made it possible to generate 3D model of complex object. To generate CAD and finite element model using point cloud data from 3D scanning, surface reconstruction applications have widely used. In the early stage, these applications have many difficulties, such as data handling, model creation time and so on. Recently developed point-based surface generation applications partly resolve these difficulties. However there are still many problems. In case of large and complex object scanning, generation of CAD and finite element model has a significant amount of working time and effort. Hence, we concerned developing a good direct finite element model generation method using point cloud's location coordinate value to save working time and obtain accurate finite element model.

A Study on Three-Dimensional Model Reconstruction Based on Laser-Vision Technology (레이저 비전 기술을 이용한 물체의 3D 모델 재구성 방법에 관한 연구)

  • Nguyen, Huu Cuong;Lee, Byung Ryong
    • Journal of the Korean Society for Precision Engineering
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    • v.32 no.7
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    • pp.633-641
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    • 2015
  • In this study, we proposed a three-dimensional (3D) scanning system based on laser-vision technique and rotary mechanism for automatic 3D model reconstruction. The proposed scanning system consists of a laser projector, a camera, and a turntable. For laser-camera calibration a new and simple method was proposed. 3D point cloud data of the surface of scanned object was fully collected by integrating extracted laser profiles, which were extracted from laser stripe images, corresponding to rotary angles of the rotary mechanism. The obscured laser profile problem was also solved by adding an addition camera at another viewpoint. From collected 3D point cloud data, the 3D model of the scanned object was reconstructed based on facet-representation. The reconstructed 3D models showed effectiveness and the applicability of the proposed 3D scanning system to 3D model-based applications.