• Journal of KIBIM
• /
• v.9 no.2
• /
• pp.21-28
• /
• 2019

Recently, many scan projects are gradually increasing for maintenance, construction. The scan data contains useful data, which can be generated in the target application from the facility, space. However, modeling the scan data required for the application requires a lot of cost. In example, the converting 3D point cloud obtained from scan data into 3D object is a time-consuming task, and the modeling task is still very manual. This research proposes Scan-to-Geometry Mapping Rule Definition (S2G-MD) which maps point cloud data to geometry for irregular building plane objects. The S2G-MD considers user use case variability. The method to define rules for mapping scan to geometry is proposed. This research supports the reverse engineering semi-automatic process for the building planar geometry from the user perspective.

• Journal of KIBIM
• /
• v.10 no.4
• /
• pp.32-39
• /
• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• 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.

• Journal of Broadcast Engineering
• /
• v.24 no.4
• /
• pp.660-669
• /
• 2019

Recently, with the development of three-dimensional scanning devices and multi-dimensional array cameras, research is continuously conducted on techniques for handling three-dimensional data in application fields such as AR (Augmented Reality) / VR (Virtual Reality) and autonomous traveling. In particular, in the AR / VR field, content that expresses 3D video as point data has appeared, but this requires a larger amount of data than conventional 2D images. Therefore, in order to serve 3D point cloud content to users, various technological developments such as highly efficient encoding / decoding and storage, transfer, etc. are required. In this paper, V-PCC bit stream created using V-PCC encoder proposed in MPEG-I (MPEG-Immersive) V-PCC (Video based Point Cloud Compression) group, It is defined by the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standard, and provides to be composed of segments. Also, in order to provide the user with the information of the 3D coordinate system, the depth information parameter of the signaling message is additionally defined. Then, we design a verification platform to verify the technology proposed in this paper, and confirm it in terms of the algorithm of the proposed technology.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.11-16
• /
• 2023

The technology of 3D reconstruction, primarily relying on point cloud data, is essential for digitizing objects or spaces. This paper aims to utilize reinforcement learning to achieve the acquisition of point clouds in a given environment. To accomplish this, a simulation environment is constructed using Unity, and reinforcement learning is implemented using the Unity package known as ML-Agents. The process of point cloud acquisition involves initially setting a goal and calculating a traversable path around the goal. The traversal path is segmented at regular intervals, with rewards assigned at each step. To prevent the agent from deviating from the path, rewards are increased. Additionally, rewards are granted each time the agent fixates on the goal during traversal, facilitating the learning of optimal points for point cloud acquisition at each traversal step. Experimental results demonstrate that despite the variability in traversal paths, the approach enables the acquisition of relatively accurate point clouds.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.3
• /
• pp.276-284
• /
• 2022

In this paper, we implemented cloud rendering using WebRTC for high-quality AR and VR services. Cloud rendering is an applied technology of cloud computing. It efficiently handles the rendering of large volumes of 3D content. The conventional VR and AR service is a method of downloading 3D content. The download time is delayed as the 3D content capacity increases. Cloud rendering is a streaming method according to the user's point of view. Therefore, stable service is possible regardless of the 3D content capacity. In this paper, we implemented cloud rendering using WebRTC and analyzed its performance. We compared latency of 100MB, 300MB, and 500MB 3D AR content in 100Mbps and 300Mbps internet environments. As a result of the analysis, cloud rendering showed stable latency regardless of data volume. On the other hand, the conventional method showed an increase in latency as the data volume increased. The results of this paper quantitatively evaluate the stability of cloud rendering. This is expected to contribute to high-quality VR and AR services

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.6
• /
• pp.542-553
• /
• 2020

Virtual world and simulation need large scale map rendering. However, rendering too many vertices is a computationally complex and time-consuming process. Some game development companies have developed 3D LOD objects for high-speed rendering based on distance between camera and 3D object. Terrain physics simulation researchers need a way to recognize the original object shape from 3D LOD objects. In this paper, we proposed simply automatic LOD framework using point cloud data (PCD). This PCD was created using a 6-direct orthographic ray. Various experiments are performed to validate the effectiveness of the proposed method. We hope the proposed automatic LOD generation framework can play an important role in game development and terrain physic simulation.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.787-800
• /
• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.12
• /
• pp.77-86
• /
• 2015

It is difficult to extract small valve automatically in noisy 3D point cloud obtained from LIDAR because small object is affected by noise considerably. In this paper, we assume that the valve is a complex model consisting of torus, cylinder and plane represents handle, rib and center plane to extract a pose of the valve. And to extract the pose, we received additional input: center of the valve. We generated histogram of distance between the center and each points of point cloud, and obtain pose of valve by extracting parameters of handle, rib and center plane. Finally, the valve is reconstructed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.445-446
• /
• 2019

Recently, attention has been focused on mixed reality (MR) technology, which provides an experience that can not be realized in reality by fusing virtual information into the real world. Mixed reality has the advantage of having excellent interaction with reality and maximizing immersion feeling. In this paper, we propose a method to acquire a point cloud for the production of mixed reality contents using multiple Depth and RGB camera system.