• Title/Summary/Keyword: point cloud

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A Fast Correspondence Matching for Iterative Closest Point Algorithm (ICP 계산속도 향상을 위한 빠른 Correspondence 매칭 방법)

  • Shin, Gunhee;Choi, Jaehee;Kim, Kwangki
    • The Journal of Korea Robotics Society
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    • v.17 no.3
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    • pp.373-380
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    • 2022
  • This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.

Region Selective Transmission Method of MMT based 3D Point Cloud Content (MMT 기반 3차원 포인트 클라우드 콘텐츠의 영역 선별적 전송 방안)

  • Kim, Doohwan;Kim, Junsik;Kim, Kyuheon
    • Journal of Broadcast Engineering
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    • v.25 no.1
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    • pp.25-35
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    • 2020
  • Recently, the development of image processing technology, as well as hardware performance, has been continuing the research on 3D point processing technology that provides users with free viewing angle and stereoscopic effect in various fields. Point cloud technology, which is a type of representation of 3D point, has attracted attention in various fields because it can acquired/expressed point precisely. However, since Hundreds of thousands, millions of point are required to represent one 3D point cloud content, there is a disadvantage that a larger amount of storage space is required than a conventional 2D content. For this reason, the MPEG (Moving Picture Experts Group), an international standardization organization, is continuing to research how to efficiently compress, store, and transmit 3D point cloud content to users. In this paper, a V-PCC bitstream generated by a V-PCC (Video-based Point Cloud Compression) encoder proposed by the MPEG-I (Immersive) group is composed of an MPU (Media Processing Unit) defined by the MMT. In addition, by extending the signaling message defined in the MMT standard, a parameter for a segmented transmission method of the 3D point cloud content by area and quality parameters considering the characteristic of the 3D point cloud content, so that the quality parameters can be selectively determined according to the user's request. Finally, in this paper, we verify the result through design/implementation of the verification platform based on the proposed technology.

A Fast Ground Segmentation Method for 3D Point Cloud

  • Chu, Phuong;Cho, Seoungjae;Sim, Sungdae;Kwak, Kiho;Cho, Kyungeun
    • Journal of Information Processing Systems
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    • v.13 no.3
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    • pp.491-499
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    • 2017
  • In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

Prerequisite Research for the Development of an End-to-End System for Automatic Tooth Segmentation: A Deep Learning-Based Reference Point Setting Algorithm (자동 치아 분할용 종단 간 시스템 개발을 위한 선결 연구: 딥러닝 기반 기준점 설정 알고리즘)

  • Kyungdeok Seo;Sena Lee;Yongkyu Jin;Sejung Yang
    • Journal of Biomedical Engineering Research
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    • v.44 no.5
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    • pp.346-353
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    • 2023
  • In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

Object Detection with LiDAR Point Cloud and RGBD Synthesis Using GNN

  • Jung, Tae-Won;Jeong, Chi-Seo;Lee, Jong-Yong;Jung, Kye-Dong
    • International journal of advanced smart convergence
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    • v.9 no.3
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    • pp.192-198
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    • 2020
  • The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

Automatic NURBS Surface Generation from Unorganized Point Cloud Data (임의의 점 군 데이터로부터 NURBS 곡면의 자동생성)

  • Yoo, Dong-Jin
    • Journal of the Korean Society for Precision Engineering
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    • v.23 no.9 s.186
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    • pp.200-207
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    • 2006
  • In this paper a new approach which combines implicit surface scheme and NURBS surface interpolation method is proposed in order to generate a complete surface model from unorganized point cloud data. In the method a base surface was generated by creating smooth implicit surface from the input point cloud data through which the actual surface would pass. The implicit surface was defined by a combination of shape functions including quadratic polynomial function, cubic polynomial functions and radial basis function using adaptive domain decomposition method. In this paper voxel data which can be extracted easily from the base implicit surface were used in order to generate rectangular net with good quality using the normal projection and smoothing scheme. After generating the interior points and tangential vectors in each rectangular region considering the required accuracy, the NURBS surface were constructed by interpolating the rectangular array of points using boundary tangential vectors which assure C$^1$ continuity between rectangular patches. The validity and effectiveness of this new approach was demonstrated by performing numerical experiments for the various types of point cloud data.

Object Detection and Localization on Map using Multiple Camera and Lidar Point Cloud

  • Pansipansi, Leonardo John;Jang, Minseok;Lee, Yonsik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.422-424
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    • 2021
  • In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

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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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Point Cloud Classification Method for Mountainous Area (산악지역 점군자료 분류기법 연구)

  • Choi, Yun-Woong;Lee, Geun-Sang;Cho, Gi-Sung
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2010.04a
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    • pp.387-388
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    • 2010
  • There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

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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.