• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.102-111
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• 2016

In this paper, we study the problem of computing smooth feature curves from CAD type point clouds models. The proposed method reconstructs feature curves from the intersections of developable strip pairs which approximate the regions along both sides of the features. The generation of developable surfaces is based on a linear approximation of the given point cloud through a variational shape approximation approach. A line segment sequencing algorithm is proposed for collecting feature line segments into different feature sequences as well as sequential groups of data points. A developable surface approximation procedure is employed to refine incident approximation planes of data points into developable strips. Some experimental results are included to demonstrate the performance of the proposed method.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.605-616
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• 2020

The construction quality control of hull blocks is of great significance for shipbuilding. The total station device is predominantly employed in traditional applications, but suffers from long measurement time, high labor intensity and scarcity of data points. In this paper, the Terrestrial Laser Scanning (TLS) device is utilized to obtain an efficient and accurate comprehensive construction information of hull blocks. To address the registration problem which is the most important issue in comparing the measurement point cloud and the design model, an automatic registration approach is presented. Furthermore, to compare the data acquired by TLS device and sparse point sets obtained by total station device, a method for key point extraction is introduced. Experimental results indicate that the proposed approach is fast and accurate, and that applying TLS to control the construction quality of hull blocks is reliable and feasible.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.148-152
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• 2007

본 연구에서는 LIDAR 포인트 자료로부터 분리된 포인트 군집의 윤곽 포인트 추출을 위하여，가상격자를 이용한 검색 영역의 제한을 통한 윤곽 포인트 추출 방식을 제안하였으며 성능을 평가하기 위해 보편적으로 사용되는 TIN을 이용한 방식과 비교하였다. 실제 건물 포인트 자료에 대하여 적용한 결과 TIN을 이용한 방식보다 빠른 처리가 가능하며 시각적인 평가를 통해 결과물의 품질 면에서도 두 가지 방식이 거의 유사함을 확인할 수 있었다.

• Journal of the Korean Chemical Society
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• v.52 no.2
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• pp.140-147
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• 2008

.A new approach for a cloud point extraction-electrothermal atomic absorption spectrometric method was used for determining bismuth. The aqueous analyte was acidified with sulfuric acid (pH 3.0-3.5). Triton X-114 was added as a surfactant and natriumdiethyldithiocarbaminat (Na-DDTC) was used as a complexing agent. After phase separation at 50oC based on the cloud point separation of the mixture, the surfactant-rich phasen was diluted using tetrahydrofuran (THF). Twenty microliters (20 L) of the enriched solution and 10 l of 0.1% (w/v) Pd(NO3)2 as chemical modifier were dispersed into the graphite tube and the analyte determined by electrothermal atomic absorption spectrometry. After optimizing extraction conditions and instrumental parameters, a preconcentration factor of 195 was obtained for a sample of only 10 mL. The detection limit was 0.04 ng ml-1 and the analytical curve was linear for the concentration range of 0.04-0.70 ng mL-1. Relative standard deviations were <5%. The method was successfully applied for the extraction and determination of bismuth in water samples.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1115-1121
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• 2012

This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

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

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.99-110
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• 2023

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

• Tunnel and Underground Space
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• v.26 no.1
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• pp.46-58
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• 2016

In this study, a LIDAR laser scanner was used to scan a rock slope around Mt. Gwanak and to produce point cloud from which directional information of rock joint surfaces shall be extracted. It was analyzed using two different algorithms, i.e. Ball Pivoting and Wrap algorithm, and four sampling intervals, i.e. raw, 2, 5, and 10 cm. The results of Fuzzy K-mean clustering were analyzed on the stereonet. As a result, the Ball Pivoting and Wrap algorithms were considered suitable for extraction of rock surface orientation. In the case of 5 cm sampling interval, both triangulation algorithms extracted the most number of the patch and patched area.

• Journal of KIISE
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• v.44 no.4
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• pp.392-398
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• 2017

The point-cloud representation of an object is performed by scanning a space through a laser scanner that is extracting a set of points, and the points are then integrated into the same coordinate system through a registration. The set of the completed registration-integrated point clouds is classified into meaningful regions, shapes, and noises through a mathematical analysis. In this paper, the aim is the matching of a curved area like a cylinder shape in 3D point-cloud data. The matching procedure is the attainment of the center and radius data through the extraction of the cylinder-shape candidates from the sphere that is fitted through the RANdom Sample Consensus (RANSAC) in the point cloud, and completion requires the matching of the curved region with the Catmull-Rom spline from the extracted center-point data using the Principal Component Analysis (PCA). Not only is the proposed method expected to derive a fast estimation result via linear and curved cylinder estimations after a center-axis estimation without constraint and segmentation, but it should also increase the work efficiency of reverse engineering.