• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1660-1665
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• 2022

Recently, many industries are using three dimensional scanning technology. As the performance of the 3D scanner gradually improves, a sampling step to reduce a point data or a remove step to remove a part determined to be noise are generally performed in post processing. However, total point data by long time scanning cannot be processed at once in spite of performing such those additional processes. In general, a method using a multi threaded environment is widely used, but as the scanning process work time increases, the processing performance gradually decreases due to various environmental conditions and accumulated operations. This paper proposes a method to initially remove point data judged to be unnecessary by calculating accumulated fast point feature histogram values from coming point data of the 3D scanner in real time. The entire 3D scanning process can be reduced using this approach.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.8
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• pp.251-260
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• 2014

3D point data is utilized in various industry domains for its high accuracy to the surface information of an object. It is substantially utilized in geography for terrain scanning and analysis. Generally, 3D point data need to be changed by Gridding which produces a regularly spaced array of z values from irregularly spaced xyz data. But it requires long processing time and high resource cost to interpolate grid coordination. Kriging interpolation in Gridding has attracted because Kriging interpolation has more accuracy than other methods. However it haven't been used frequently since a processing is complex and slow. In this paper, we presented a parallel Gridding algorithm which contains Kriging and an application of grid data structure to fit MapReduce paradigm to this algorithm. Experiment was conducted for 1.6 and 4.3 billions of points from Airborne LiDAR files using our proposed MapReduce structure and the results show that the total execution time is decreased more than three times to the convention sequential program on three heterogenous clusters.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.550-555
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• 2003

최근에 Lidar 데이터를 이용한 3차원 위치 정보와 지표면 속성 정보를 취득하는 연구가 많이 진행되고 있다. 높은 위치 정확도, 3차원 데이터 동시 취득, 기존 측정 방식에 비하여 점 데이터 취득의 자동화, 데이터 정확도의 안정성 등으로 인하여 복잡한 지형 및 인공구조물이 존재하는 지역에서 Lidar 데이터의 응용 사례가 많이 나타나고 있으며, 특히 건물 모델링에서 반자동 방식의 디지털 사진측량에 비하여 자동 모델링의 가능성을 보여주고 있다. 일반적으로 Lidar 데이터의 점밀도는 1점/㎡이내이며, 촬영된 스트립을 중복시켜 점밀도를 높이기도 한다. 건물은 크기와 형태가 다양하기 때문에 모델링에 필요한 점밀도를 제시하기는 어렵지만 5점 내외에서 모델링이 가능하다고 알려져 있으며 건물이외에 다른 지형지물에 대한 모델링 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 Lidar 데이터의 점밀도에 따라 지물의 모델링 가능성을 평가하고 효율적인 데이터 취득 방안을 제시하고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.47-48
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• 2021

본 논문에서는 대용량의 3D 데이터 시퀀스의 압축을 진행한다. 3D 데이터 시퀀스의 각 프레임에서 Pose Estimation을 통해 3D Skeleton을 추출한 뒤, 포인트 클라우드를 skeleton에 묶는 리깅 과정을 거치고, 다음 프레임과 같은 자세로 deformation을 진행한다. 다음 프레임과 같은 자세로 변형된 포인트 클라우드와 실제 다음 프레임의 포인트 클라우드를 비교하여, 두 데이터에 모두 있는 점, 실제 다음 프레임에만 있는 점, deformation한 데이터에만 있는 점으로 분류한다. 두 데이터에 모두 있는 점을 제외하고 나머지 두 분류의 점들을 저장함으로써 3D 시퀀스 데이터를 압축할 수 있다.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1257-1263
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• 2012

An implementation of animation module using the 3D body data scanned by laser scanner is reported in this paper. Characteristic points of the skeleton in human body were picked up as pivot point for 3D rotation. The body data set wes reconstructed as objects built in hierarchical tree structure, which is based on skeleton model. In order to implement the 3D animation of the laser scanned body data, the vertexes of the objects were connected as skeleton structure and animated to follow dynamic patterns inputted by user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.204-207
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• 2009

This paper presents how to implement Markerless Augmented Reality and how to create and apply reference data sets. There are three parts related with implementation: setting camera, creation of reference data set, and tracking. To create effective reference data sets, we need a 3D model such as CAD model. It is also required to create reference data sets from various viewpoints. We extract the feature points from the model image and then extract 3D positions corresponding to the feature points using ray tracking. These 2D/3D correspondence point sets constitute a reference data set of the model. Reference data sets are constructed for various viewpoints of the model. Fast tracking can be done using a reference data set the most frequently matched with feature points of the present frame and model data near the reference data set.

• The KIPS Transactions:PartA
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• v.17A no.6
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• pp.281-288
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• 2010

In this paper, we describe the development of a system for generating a 3-dimensional human face using 3D scanned facial data and photo images, and morphing animation. The system comprises a facial feature input tool, a 3-dimensional texture mapping interface, and a 3-dimensional facial morphing interface. The facial feature input tool supports texture mapping and morphing animation - facial morphing areas between two facial models are defined by inputting facial feature points interactively. The texture mapping is done first by means of three photo images - a front and two side images - of a face model. The morphing interface allows for the generation of a morphing animation between corresponding areas of two facial models after texture mapping. This system allows users to interactively generate morphing animations between two facial models, without programming, using 3D scanned facial data and photo images.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.163-167
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• 2009

3D human body scan technique is known to be practically useful in industrial field as the technique becomes more precise and cheaper. Landmark extraction is essential for full utilization of the scan data. In this paper, we suggest an algorithm for automatic landmark extraction. For this purpose, we perform markerless matching to the target data using PCA analysis and quasi-Newton optimization. Landmarks are extracted from the topology of resulting body.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.599-607
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• 2002

The octree model, a hierarchical volume description of 3D objects, nay be utilized to generate projected images from arbitrary viewing directions, thereby providing an efficient means of the data base for 3D object recognition and other applications. We present 2D projected image and made pseudo gray image of object using octree model and multi level boundary search algorithm. We present algorithm for finding feature points of 2D and 3D image and finding matched points using geometric transformation. The algorithm is made of data base, it will be widely applied to 3D object modeling and efficient feature points application for basic 3D object research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2335-2340
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• 2009

This paper presents how to implement Markerless Augmented Reality and how to create and apply reference data sets. There are three parts related with implementation: setting camera, creation of reference data set, and tracking. To create effective reference data sets, we need a 3D model such as CAD model. It is also required to create reference data sets from various viewpoints. We extract the feature points from the mode1 image and then extract 3D positions corresponding to the feature points using ray tracking. These 2D/3D correspondence point sets constitute a reference data set of the model. Reference data sets are constructed for various viewpoints of the model. Fast tracking can be done using a reference data set the most frequently matched with feature points of the present frame and model data near the reference data set.