• Title, Summary, Keyword: 3D Point cloud

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Massive 3D Point Cloud Visualization by Generating Artificial Center Points from Multi-Resolution Cube Grid Structure (다단계 정육면체 격자 기반의 가상점 생성을 통한 대용량 3D point cloud 가시화)

  • Yang, Seung-Chan;Han, Soo Hee;Heo, Joon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.4
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    • pp.335-342
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    • 2012
  • 3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

End-to-End based 3D Model Generation Method using a Single LiDAR (단일 LiDAR를 활용한 End-to-End 기반 3D 모델 생성 방법)

  • Kwak, Jeonghoon;Sung, Yunsick
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.532-533
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    • 2020
  • 원격 및 가상환경에서 사용자의 동작에 따른 3D 모델을 제공하기 위해 light detection and range (LiDAR)로 측정된 3D point cloud로 사용자의 3D 모델이 생성되어 원격 및 가상환경에 사용자의 모습이 제공된다. 하지만 3D 모델을 생성하기 위해서는 사용자의 신체 전부가 측정된 3D point cloud가 필요하다. 사용자의 신체 전체를 측정하기 위해서는 적어도 두 개 이상의 LiDAR가 필요하다. 두 개 이상의 LiDAR을 사용할 경우에는 LiDAR을 사용할 공간과 LiDAR를 구비하기 위한 비용이 발생한다. 단일 LiDAR로 3D 모델을 생성하는 방법이 요구된다. 본 논문에서는 단일 LiDAR에서 측정된 3D point cloud를 이용하여 3D 모델을 생성하는 방법이 제안된다. End-to-End 기반 Convolutional Neural Network (CNN) 모델로 측정된 3D point cloud를 분석하여 사용자의 체형과 자세를 예측하도록 학습한다. 기본자세를 취하는 동안 수집된 3D point cloud로 기본이 되는 사용자의 3D 모델을 생성한다. 학습된 CNN 모델을 통하여 측정된 3D point cloud로 사용자의 자세를 예측하여 기본이 되는 3D 모델을 수정하여 3D 모델을 제공한다.

Three-Dimensional Face Point Cloud Smoothing Based on Modified Anisotropic Diffusion Method

  • Wibowo, Suryo Adhi;Kim, Sungshin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.2
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    • pp.84-90
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    • 2014
  • This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

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.

Sequential Point Cloud Generation Method for Efficient Representation of Multi-view plus Depth Data (다시점 영상 및 깊이 영상의 효율적인 표현을 위한 순차적 복원 기반 포인트 클라우드 생성 기법)

  • Kang, Sehui;Han, Hyunmin;Kim, Binna;Lee, Minhoe;Hwang, Sung Soo;Bang, Gun
    • Journal of Korea Multimedia Society
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    • v.23 no.2
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    • pp.166-173
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    • 2020
  • Multi-view images, which are widely used for providing free-viewpoint services, can enhance the quality of synthetic views when the number of views increases. However, there needs an efficient representation method because of the tremendous amount of data. In this paper, we propose a method for generating point cloud data for the efficient representation of multi-view color and depth images. The proposed method conducts sequential reconstruction of point clouds at each viewpoint as a method of deleting duplicate data. A 3D point of a point cloud is projected to a frame to be reconstructed, and the color and depth of the 3D point is compared with the pixel where it is projected. When the 3D point and the pixel are similar enough, then the pixel is not used for generating a 3D point. In this way, we can reduce the number of reconstructed 3D points. Experimental results show that the propose method generates a point cloud which can generate multi-view images while minimizing the number of 3D points.

Real-time Polygon Generation and Texture Mapping for Tele-operation using 3D Point Cloud Data (원격 작업을 위한 3 차원 점군 데이터기반의 실시간 폴리곤 생성 및 텍스처 맵핑 기법)

  • Jang, Ga-Ram;Shin, Yong-Deuk;Yoon, Jae-Shik;Park, Jae-Han;Bae, Ji-Hun;Lee, Young-Soo;Baeg, Moon-Hong
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.10
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    • pp.928-935
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    • 2013
  • In this paper, real-time polygon generation algorithm of 3D point cloud data and texture mapping for tele-operation is proposed. In a tele-operation, it is essential to provide more highly realistic visual information to a tele-operator. By using 3D point cloud data, the tele-operator can observe the working environment from various view point with a reconstructed 3D environment. However, there are huge empty space in 3D point cloud data, since there is no environmental information among the points. This empty space is not suitable for an environmental information. Therefore, real-time polygon generation algorithm of 3D point cloud data and texture mapping is presented to provide more highly realistic visual information to the tele-operator. The 3D environment reconstructed from the 3D point cloud data with texture mapped polygons is the crucial part of the tele-operation.

Supporting ROI transmission of 3D Point Cloud Data based on 3D Manifesto (3차원 Manifesto 기반 3D Point Cloud Data의 ROI 전송 지원 방안)

  • Im, Jiehon;Kim, Junsik;Rhyu, Sungryeul;Kim, Hoejung;Kim, Sang IL;Kim, Kyuheon
    • Journal of the Semiconductor & Display Technology
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    • v.17 no.4
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    • pp.21-26
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    • 2018
  • Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to be applied to applications such as AR, VR, and autonomous mobile vehicles that deal with 3D data. In Particular, the 3D point cloud data consisting of tens to hundreds of thousands of 3D points is rapidly increased in capacity compared with 2D data, Efficient encoding / decoding technology for smooth service within a limited bandwidth, and efficient service provision technology for differentiating the area of interest and the surrounding area are needed. In this paper, we propose a new quality parameter considering characteristics of 3D point cloud instead of quality change based on assumed video codec in MPEG V-PCC used in 3D point cloud compression, 3D Grid division method and representation for selectively transmitting 3D point clouds according to user's area of interest, and propose a new 3D Manifesto. By using the proposed technique, it is possible to generate more bitrate images, and it is confirmed that the efficiency of network, decoder, and renderer can be increased while selectively transmitting as needed.

Complete 3D Surface Reconstruction from Unstructured Point Cloud

  • Kim, Seok-Il;Li, Rixie
    • Journal of Mechanical Science and Technology
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    • v.20 no.12
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    • pp.2034-2042
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    • 2006
  • In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices, of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced. The control mesh model for well representing the topology of point cloud is derived from the initial mesh model by using the mesh simplification technique based on the original QEM algorithm, and the parametric surface model for approximately representing the geometry of point cloud is derived by applying the local subdivision surface fitting scheme on the control mesh model. And, to reconstruct the complete matching surface model, the insertion of isolated points on the parametric surface model and the mesh optimization are carried out. Especially, the fast 3D surface reconstruction is realized by introducing the voxel-based nearest-point search algorithm, and the simulation results reveal the availability of the proposed surface reconstruction method.

Density Scalability of Video Based Point Cloud Compression by Using SHVC Codec (SHVC 비디오 기반 포인트 클라우드 밀도 스케일러빌리티 방안)

  • Hwang, Yonghae;Kim, Junsik;Kim, Kyuheon
    • Journal of Broadcast Engineering
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    • v.25 no.5
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    • pp.709-722
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    • 2020
  • Point Cloud which is a cluster of numerous points can express 3D object beyond the 2D plane. Each point contains 3D coordinate and color data basically, reflectance or etc. additionally. Point Cloud demand research and development much higher effective compression technology. Video-based Point Cloud Compression (V-PCC) technology in development and standardization based on the established video codec. Despite its high effective compression technology, point cloud service will be limited by terminal spec and network conditions. 2D video had the same problems. To remedy this kind of problem, 2D video is using Scalable High efficiency Video Coding (SHVC), Dynamic Adaptive Streaming over HTTP (DASH) or diverse technology. This paper proposed a density scalability method using SHVC codec in V-PCC.