• Title, Summary, Keyword: Point Cloud Data

Search Result 329, Processing Time 0.032 seconds

Development of Classification Technique of Point Cloud Data Using Color Information of UAV Image

  • Song, Yong-Hyun;Um, Dae-Yong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.35 no.4
    • /
    • pp.303-312
    • /
    • 2017
  • This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image's information. Furthermore, it was possible to classify particular object's point cloud data in high classification accuracy.

Fusing Algorithm for Dense Point Cloud in Multi-view Stereo (Multi-view Stereo에서 Dense Point Cloud를 위한 Fusing 알고리즘)

  • Han, Hyeon-Deok;Han, Jong-Ki
    • Journal of Broadcast Engineering
    • /
    • v.25 no.5
    • /
    • pp.798-807
    • /
    • 2020
  • As technologies using digital camera have been developed, 3D images can be constructed from the pictures captured by using multiple cameras. The 3D image data is represented in a form of point cloud which consists of 3D coordinate of the data and the related attributes. Various techniques have been proposed to construct the point cloud data. Among them, Structure-from-Motion (SfM) and Multi-view Stereo (MVS) are examples of the image-based technologies in this field. Based on the conventional research, the point cloud data generated from SfM and MVS may be sparse because the depth information may be incorrect and some data have been removed. In this paper, we propose an efficient algorithm to enhance the point cloud so that the density of the generated point cloud increases. Simulation results show that the proposed algorithm outperforms the conventional algorithms objectively and subjectively.

Template-Based Reconstruction of Surface Mesh Animation from Point Cloud Animation

  • Park, Sang Il;Lim, Seong-Jae
    • ETRI Journal
    • /
    • v.36 no.6
    • /
    • pp.1008-1015
    • /
    • 2014
  • In this paper, we present a method for reconstructing a surface mesh animation sequence from point cloud animation data. We mainly focus on the articulated body of a subject - the motion of which can be roughly described by its internal skeletal structure. The point cloud data is assumed to be captured independently without any inter-frame correspondence information. Using a template model that resembles the given subject, our basic idea for reconstructing the mesh animation is to deform the template model to fit to the point cloud (on a frame-by-frame basis) while maintaining inter-frame coherence. We first estimate the skeletal motion from the point cloud data. After applying the skeletal motion to the template surface, we refine it to fit to the point cloud data. We demonstrate the viability of the method by applying it to reconstruct a fast dancing motion.

Extraction of Geometric Primitives from Point Cloud Data

  • Kim, Sung-Il;Ahn, Sung-Joon
    • 제어로봇시스템학회:학술대회논문집
    • /
    • /
    • pp.2010-2014
    • /
    • 2005
  • Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

  • PDF

Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

  • Sun, Jingyu;Hiekata, Kazuo;Yamato, Hiroyuki;Nakagaki, Norito;Sugawara, Akiyoshi
    • Journal of Computational Design and Engineering
    • /
    • v.1 no.3
    • /
    • pp.202-212
    • /
    • 2014
  • To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

A Study on a Lossless Compression Scheme for Cloud Point Data of the Target Construction (목표 구조물에 대한 점군데이터의 무손실 압축 기법에 관한 연구)

  • Bang, Min-Suk;Yun, Kee-Bang;Kim, Ki-Doo
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.48 no.5
    • /
    • pp.33-41
    • /
    • 2011
  • In this paper, we propose a lossless compression scheme for cloud point data of the target construction by using doubleness and decreasing useless information of cloud point data. We use Hough transform to find the horizontal angle between construction and terrestrial LIDAR. This angle is used for the rotation of the cloud point data. The cloud point data can be parallel to x-axis, then y-axis doubleness is increased. Therefore, the cloud point data can be more compressed. In addition, we apply two methods to decrease the number of cloud point data for useless information of them. One is decimation of the cloud point data, the other is to extract the range of y-coordinates of target construction, and then extract the cloud point data existing in the range only. The experimental result shows the performance of proposed scheme. To compress the data, we use only the position information without additional information. Therefore, this scheme can increase processing speed of the compression algorithm.

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
    • /
    • v.14 no.2
    • /
    • pp.84-90
    • /
    • 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.

Palette-based Color Attribute Compression for Point Cloud Data

  • Cui, Li;Jang, Euee S.
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.13 no.6
    • /
    • pp.3108-3120
    • /
    • 2019
  • Point cloud is widely used in 3D applications due to the recent advancement of 3D data acquisition technology. Polygonal mesh-based compression has been dominant since it can replace many points sharing a surface with a set of vertices with mesh structure. Recent point cloud-based applications demand more point-based interactivity, which makes point cloud compression (PCC) becomes more attractive than 3D mesh compression. Interestingly, an exploration activity has been started to explore the feasibility of PCC standard in MPEG. In this paper, a new color attribute compression method is presented for point cloud data. The proposed method utilizes the spatial redundancy among color attribute data to construct a color palette. The color palette is constructed by using K-means clustering method and each color data in point cloud is represented by the index of its similar color in palette. To further improve the compression efficiency, the spatial redundancy between the indices of neighboring colors is also removed by marking them using a flag bit. Experimental results show that the proposed method achieves a better improvement of RD performance compared with that of the MPEG PCC reference software.

A Study on Cross-sectioning Methods for Measured Point Data (측정 점데이터로부터 단면 데이터 추출에 관한 연구)

  • 우혁제;강의철;이관행
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • /
    • pp.272-276
    • /
    • 2000
  • Reverse engineering refers to the process that creates a physical part from acquiring the surface data of an existing part using a scanning device. In recent years, as the non-contact type scanning devices become more popular, the huge amount of point data can be obtained with high speed. The point data handling process, therefore, becomes more important since the scan data need to be refined for the efficiency of subsequent tasks such as mesh generation and surface fitting. As one of point handling functions, the cross-sectioning function is still frequently used for extracting the necessary data from the point cloud. The commercial reverse engineering software supports cross-sectioning functions, however, these are only for cross-sectioning the point cloud with the constant spacing and direction. In this paper, adaptive cross-sectioning point cloud which allow the changes of the spacing and directions of cross-sections according to the constant spacing and direction. In this paper, adaptive cross-sectioning algorithms which allow the changes of the spacing and directions of cross-sections according to the curvature difference of the point cloud data are proposed.

  • PDF

A Basic Study on Trade-off Analysis of Downsampling for Indoor Point Cloud Data (실내 포인트 클라우드 데이터 Downsampling의 Trade-off 분석을 통한 기초 연구)

  • Kang, Nam-Woo;Oh, Sang-Min;Ryu, Min-Woo;Jung, Yong-Gil;Cho, Hun-hee
    • Proceedings of the Korean Institute of Building Construction Conference
    • /
    • /
    • pp.40-41
    • /
    • 2020
  • As the capacity of the 3d scanner developed, the reverse engineering using the 3d scanner is emphasized in the construction industry to obtain the 3d geometric representation of buildings. However, big size of the indoor point cloud data acquired by the 3d scanner restricts the efficient process in the reverse engineering. In order to solve this inefficiency, several pre-processing methods simplifying and denoising the raw point cloud data by the rough standard are developed, but these non-standard methods can cause the inaccurate recognition and removal the key-points. This paper analyzes the correlation between the accuracy of wall recognition and the density of the data, thus proposes the proper method for the raw point cloud data. The result of this study could improve the efficiency of the data processing phase in the reverse engineering for indoor point cloud data.

  • PDF