• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 봄 학술논문 발표대회
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• pp.40-41
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• 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.

• 제어로봇시스템학회논문지
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• 제9권9호
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• pp.714-719
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• 2003

We propose a measuring method of large object using the pattern matching. It is hard and expensive to get the complete 3D data when the object is large and exceeds the limit of measuring devices. The large object is divided into several smaller areas and is scanned several times to get the data of all the pieces. These data are aligned to get the complete 3D data using the pattern matching method such as point pattern matching method and transform matrix algorithm. The laser slit beam and CCD camera are applied for the experimental measurement. Visual C＋＋ on Windows 98 is implemented in processing the algorithm.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.836-838
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• 2022

3D Point Cloud는 3D 콘텐츠를 더욱 실감 나게 표현하기 위한 데이터 포맷이다. Point Cloud 데이터는 3차원 공간상에 존재하는 데이터로 기존의 2D 영상에 비해 거대한 용량을 가지고 있다. 최근 대용량 Point Cloud의 3D 데이터를 압축하기 위해 V-PCC(Video-based Point Cloud Compression)와 같은 다양한 방법이 제시되고 있다. 따라서 Point Cloud 데이터의 원활한 전송 및 저장을 위해서는 V-PCC와 같은 압축 기술이 요구된다. V-PCC는 Point Cloud의 데이터들을 Patch로써 뜯어내고 2D에 Projection 시켜 3D의 영상을 2D 형식으로 변환하고 2D로 변환된 Point Cloud 영상을 기존의 2D 압축 코덱을 활용하여 압축하는 기술이다. 이 V-PCC로 변환된 2D 영상은 기존 2D 영상을 전송하는 방식을 활용하여 네트워크 기반 전송이 가능하다. 본 논문에서는 V-PCC 방식으로 압축한 V3C 데이터를 방송망으로 전송 및 소비하기 위해 MPEG Media Transport(MMT) Packet을 만드는 패킷화 방안을 제안한다. 또한 Server와 Client에서 주고받은 V3C(Visual Volumetric Video Coding) 데이터의 비트스트림을 비교하여 검증한다.

• 한국정밀공학회지
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• 제23권5호
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• pp.143-148
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• 2006

It is still very difficult to generate a geometry model and finite element model, which has complex and many free surface, even though 3D CAD solutions are applied. Furthermore, in the medical field, which is a big growth area of recent years, there is no drawing. For these reasons, making a geometry model, which is used in finite element analysis, is very difficult. To resolve these problems and satisfy the requests of the need to create a 3D digital file for an object where none had existed before, new technologies are appeared recently. Among the recent technologies, there is a growing interest in the availability of fast, affordable optical range laser scanning. The development of 3D laser scan technology to obtain 3D point cloud data, made it possible to generate 3D model of complex object. To generate CAD and finite element model using point cloud data from 3D scanning, surface reconstruction applications have widely used. In the early stage, these applications have many difficulties, such as data handling, model creation time and so on. Recently developed point-based surface generation applications partly resolve these difficulties. However there are still many problems. In case of large and complex object scanning, generation of CAD and finite element model has a significant amount of working time and effort. Hence, we concerned developing a good direct finite element model generation method using point cloud's location coordinate value to save working time and obtain accurate finite element model.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• 방송공학회논문지
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• 제26권6호
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• pp.680-691
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• 2021

최근 3차원 스캐너 등을 이용한 3차원 영상 획득 기술이 발전함에 따라 AR(Augmented Reality)/VR(Virtual Reality) 분야에서 활용되는 콘텐츠가 다양해졌다. 이러한 3차원 영상을 나타내는 방식에는 여러 가지가 존재하며, 포인트 클라우드는 그중 하나다. 포인트 클라우드는 3차원 공간에 존재하는 물체를 표현하는 점들의 집합을 의미하고, 실제 객체를 촬영하여 정밀하게 데이터를 획득 및 표현할 수 있다는 장점이 있다. 하지만, 3차원 영상의 특성상 2차원 영상보다는 표현해야 하는 데이터가 많고, 특히 여러 장의 프레임으로 구성된 동적인 3차원 객체는 더욱 많은 데이터를 요구하기에 이를 효율적으로 다루기 위한 고효율의 압축 기술이 개발되어야 한다. 본 논문에서는 도메인 변환 방법인 3D DCT(3-Dimensional Discrete Cosine Transform)를 이용한 움직임 예측을 통하여 포인트 클라우드 영상의 I 프레임 및 P 프레임을 효율적으로 압축하는 기술을 제안한다. 그리고 본 논문에서 제안된 기술과 Intra 프레임 기반의 배경 기술 및 2D DCT 기반의 V-PCC(Video-based Point Cloud Compression)와의 비교를 통해 제안 기술의 압축 성능을 확인한다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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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.

• 한국측량학회지
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• 제30권4호
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• pp.335-342
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• 2012

건축, 토목, 의료, 컴퓨터 그래픽스 분야 등 다양한 분야에서 이용되는 3D point cloud는 최근 레이저 스캐너의 발달로 인해 그 용량이 점점 커지게 되었다. 컴퓨터 메모리의 용량을 넘어서서 모든 데이터를 한 번에 처리할 수 없는 대용량 3D point cloud를 가시화하고 편집하기 위해 여러 전처리 및 가시화 방법들이 소개되었고 본 논문에서 비교한 QSplat의 경우 3D 모델의 형상 확인과 용량 감소를 목적으로 원본 좌표를 손실 압축하여 저장하였다. 본 논문에서 제시하는 방법은 3D point cloud를 정육면체 격자로 분할하고 center sampling을 통해 가상점 집합을 생성하며 가시화 과정에서 격자에 저장된 point 집합 취득을 통한 빠른 렌더링이 가능하다. 홍익대학교 인근 지역을 측정한 약 1억 2천만 개 point의 대용량 3D point cloud를 QSplat과 다단계 정육면체 격자 기반 방법으로 비교한 결과 전처리 과정에서는 QSplat이, 가시화 과정에서는 다단계 정육면체 격자 기반 방법이 빠른 속도를 보여주었다. 또한 다단계 정육면체 격자 기반 방법은 point의 원본 좌표를 저장하기에 추후 가시화 외에 편집, segmentation 등의 작업을 고려하여 고안되었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.787-800
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• 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.