• 방송공학회논문지
• /
• 제25권5호
• /
• pp.798-807
• /
• 2020

디지털 카메라와 휴대폰 카메라의 발달로 인해 이미지를 기반으로 3차원 물체를 복원하는 기술이 크게 발전했다. 하지만 Structure-from-Motion(SfM)과 Multi-view Stereo(MVS)를 이용한 결과인 dense point cloud에는 여전히 듬성한 영역이 존재한다. 이는 깊이 정보를 추정하는데 있는 어려움과, 깊이 지도를 point cloud로 fusing할 때 이웃 영상과의 깊이 정보가 불일치할 경우 깊이 정보를 삭제하고 point를 생성하지 않았기 때문이다. 본 논문에선 평면을 모델링하여 삭제된 깊이 정보에 새로운 깊이 정보를 부여하고 point를 생성하여 기존 결과보다 dense한 point cloud를 생성하는 알고리즘을 제안한다. 실험 결과를 통해 제안하는 알고리즘이 효과적으로 기존의 방법보다 dense한 point cloud를 생성함을 확인할 수 있다.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2020년도 봄 학술논문 발표대회
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권6호
• /
• 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.

• 한국산업융합학회 논문집
• /
• 제25권3호
• /
• pp.397-406
• /
• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• 대한조선학회논문집
• /
• 제61권3호
• /
• pp.143-151
• /
• 2024

In this study, we present a mapping framework for 3D spatial reconstruction of digital twin model using navigation and perception sensors mounted on an Autonomous Surface Vehicle (ASV). For improving the level of realism of digital twin models, 3D spatial information should be reconstructed as a digitalized spatial model and integrated with the components and system models of the ASV. In particular, for the 3D spatial reconstruction, color and 3D point cloud data which acquired from a camera and a LiDAR sensors corresponding to the navigation information at the specific time are required to map without minimizing the noise. To ensure clear and accurate reconstruction of the acquired data in the proposed mapping framework, a image preprocessing was designed to enhance the brightness of low-light images, and a preprocessing for 3D point cloud data was included to filter out unnecessary data. Subsequently, a point matching process between consecutive 3D point cloud data was conducted using the Generalized Iterative Closest Point (G-ICP) approach, and the color information was mapped with the matched 3D point cloud data. The feasibility of the proposed mapping framework was validated through a field data set acquired from field experiments in a inland water environment, and its results were described.

• 한국정보통신학회논문지
• /
• 제23권11호
• /
• pp.1482-1484
• /
• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.

• 대한조선학회논문집
• /
• 제59권5호
• /
• pp.330-337
• /
• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

• 제어로봇시스템학회논문지
• /
• 제9권9호
• /
• pp.700-706
• /
• 2003

This paper presents a 3D last design system that provides the 3-dimensional last data based on the FFD(Free Form Deformation) method. The proposed system utilizes the control points for deformation factor to convert from the 3D point cloud foot data to the 3D point cloud last data. The deformation factor of the FFD is obtained from the conventional last design technique, and constructed on the FFD lattice based on the bottom view and lateral view of the measured 3D point cloud foot data. In addition, the control points of FFD lattice is decided on the anatomical points of foot. The deformed 3D last obtained from the proposed FFD is saved as a 3D dxf foot data. The experimental results demonstrate that the proposed system have the descent 3D last data based on the openGL window.

• 한국지리정보학회지
• /
• 제21권4호
• /
• pp.132-144
• /
• 2018

건설, 의료, 컴퓨터 그래픽스, 도시공간 관리 등 다양한 분야에서 3차원 공간정보 모델이 이용되고 있다. 특히 측량 및 공간정보 분야에서는 최근 고품질의 3차원 공간정보와 실내공간정보에 대한 수요가 폭발적으로 증가하고 있으나, 현재 공간정보 데이터가 다양한 형식과 저장구조로 구성되어 관리되고 있어 저비용 고효율의 3차원 공간정보 서비스가 어려운 상황이다. 실제로 활용도 높은 3차원 모델을 구축하기 위한 기술은 관측과 처리에 고액의 비용이 발생하지만, 대부분의 수요처에서는 이러한 고비용의 공간정보 구축에 어려움을 느끼는 경우가 대부분이다. 따라서 본 연구에서는 저비용의 3D 공간정보 모델을 구축하기 위한 효율적인 방안을 제시하는 것을 목적으로 하였다. 현재의 3D 모델의 구축 방법 중 가장 효율적인 방법으로는 기존에 구축되어 있는 Point Cloud, UAV 관측영상 등의 원시데이터를 활용하여 비용을 절감시키는 방법이 있지만, 이는 관리하는 기관이 분리되어 있고 사용하기 위해 요청하는 절차가 복잡하여 활용에 제한이 있었다. 본 연구에서는 이를 해결하기 위해서 도로대장 관리 분야를 대상으로 3D 구축에 필요한 기반데이터를 통합하여 연계하고 관리 할 수 있는 통합관리 시스템 개발을 수행하였으며, 다양한 형태의 원시자료를 Point Cloud 형식으로 구성하여 도로대장 관리에 적용할 경우 6개의 주요 관리항목을 효과적 구축 및 관리할 수 있을 것으로 판단되었다.

• 대한임베디드공학회논문지
• /
• 제16권5호
• /
• pp.215-223
• /
• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.