• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.611-622
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• 2020

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

• Cartoon and Animation Studies
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• s.48
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• pp.199-215
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• 2017

Recently, a drone is used for the general purpose application although the drone was builtfor the military purpose. A drone is actively used for the creation of contents, and an image acquisition. In this paper, we develop a 3D library module platform using 3D mesh model data, which is generated by a drone image and its point cloud. First, a lot of 2D image data are taken by a drone, and a point cloud data is generated from 2D drone images. A 3D mesh data is acquired from point cloud data. Then, we develop a service library platform using a transformed 3D data for multi-purpose uses. Our platform with 3D data can minimize the cost and time of contents creation for special effects during the production of a movie, drama, or documentary. Our platform can contribute the creation of experts for the digital contents production in the field of a realistic media, a special image, and exhibitions.

• Annual Conference of KIPS
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• 2024.10a
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• pp.647-648
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• 2024

최근 몇 년의 3D, 텍스트의 멀티 모달 연구의 방향성을 파악하고, 3D 포인트 클라우드 데이터와 텍스트 사이의 연관성을 파악할 수 있는 새로운 방법론을 제시한다.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.428-435
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• 2024

In this paper, a human activity regeneration (HAR) system based on multiple input multiple output frequency modulation continuous wave (MIMO FMCW) radar was designed and implemented. Using point cloud data from MIMO radar sensors has advantages in terms of privacy, safety, and accuracy. For the implementation of the HAR system, a customized neural network based on PointPillars and depthwise separate convolutional neural network (DS-CNN) was developed. By processing high-resolution point cloud data through a lightweight network, high accuracy and efficiency were achieved. As a result, the accuracy of 98.27% and the computational complexity of 11.27M multiply-accumulates (Macs) were achieved. In addition, the developed neural network model was implemented on Raspberry-Pi embedded system and it was confirmed that point cloud data can be processed at a speed of up to 8 fps.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.727-736
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• 2019

In this paper, we present how to create a point cloud for a target area using multiple unmanned aerial vehicle images and to remove the gaps and overlapping points between datasets. For this purpose, first, IBA (Incremental Bundle Adjustment) technique was applied to correct the position and attitude of UAV platform. We generate a point cloud by using MDR (Multi-Dimensional Relaxation) matching technique. Next, we register point clouds based on observation vectors between stereo images by doing this we remove gaps between point clouds which are generated from different stereo pairs. Finally, we applied an occupancy grids based integration algorithm to remove duplicated points to create an integrated point cloud. The experiments were performed using UAV images, and our experiments show that it is possible to remove gaps and duplicate points between point clouds generated from different stereo pairs.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.219-220
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• 2019

본 논문에서는 특징점 기반의 포인트 클라우드 정합 알고리즘을 제안한다. 컴퓨터 비전 분야에서 각각 다른 카메라에서 획득한 데이터를 하나의 통합된 데이터로 정합하는 문제에 많은 관심을 두고 있다. 기존의 방법들은 큰 오차를 가지고 있거나 많은 카메라 대수나 고가의 RGB-D 카메라를 필요로 한다. 본 논문에서는 깊이 카메라에서 얻은 깊이 영상과 색상 영상을 이용하고 함수 최적화 알고리즘을 적용해 저가의 RGB-D 카메라 8대를 이용하여 오차가 적은 포인트 클라우드 정합 방법을 제안한다.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.204-211
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• 2021

In this study, information on vegetation was collected using a point cloud through a 3-D Terrestrial Lidar Scanner, and the physical shape was analyzed by reconfiguring the object based on the refined data. Each filtering step of the raw data was optimized, and the reference volume and the estimated results using the Alpha Shape and Voxel techniques were compared. As a result of the analysis, when the volume was calculated by applying the Alpha Shape, it was overestimated than reference volume regardless of data filtering. In addition, the Voxel method to be the most similar to the reference volume after the 8th filtering, and as the filtering proceeded, it was underestimated. Therefore, when re-implementing an object using a point cloud, internal voids due to the complex shape of the target object must be considered, and it is necessary to pay attention to the filtering process for optimal data analyzed in the filtering process.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1349-1365
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• 2020

In recent years, in the fields of smart cities and digital twins, research on model generation is increasing due to the advantage of acquiring actual 3D coordinates by using point clouds. In addition, there is an increasing demand for a solid model that can easily modify the shape and texture of the building. In this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. Accordingly, in this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. In the first step, the ground points were removed through the planarity analysis of the point cloud. In the second step, building area was extracted from the ground removed point cloud. In the third step, detailed structural area of the buildings was extracted. In the fourth step, the shape of 3D building models with 3D coordinate information added to the extracted area was created. In the last step, a 3D building solid model was created by giving texture to the building model shape. In order to verify the proposed method, we experimented using point clouds extracted from unmanned aerial vehicle images using commercial software. As a result, 3D building shapes with a position error of about 1m compared to the point cloud was created for all buildings with a certain height or higher. In addition, it was confirmed that 3D models on which texturing was performed having a resolution of less than twice the resolution of the original image was generated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.692-698
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• 2016

Recently, the facility extension construction and maintenance market portion has increased instead of decreased the newly facility construction. In this context, it is important to examine the reverse engineering of MEP (Mechanical Electrical and Plumbing) facilities, which have the high operation and management cost in the architecture domains. The purpose of this study was to suggest the Large Point Cloud-based Pipe Shape Reverse Engineering Method. To conduct the study, the related researches were surveyed and the reverse engineering automation method of the pipe shapes considering large point cloud was proposed. Based on the method, the prototype was developed and the results were validated. The proposed method is suitable for large data processing considering the validation results because the rendering performance standard deviation related to the 3D point cloud massive data searching was 0.004 seconds.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.500-504
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• 2020

포인트 클라우드는 수십만 또는 수백만개의 포인트로 객체 또는 장면을 나타내며, 그 데이터의 양은 엄청 나기 때문에, 다양한 대역폭 또는 장치에서 효과적인 서비스를 위해 확장성 기능을 갖춘 압축 체계 개발이 필요하다. 이에 따라, 단방향 패치 패킹을 활용한 LoD 제어 테이블 기반 밀도 확장성(LoD control table based Density scalability by using Unidirectional Patch packing, LDUP) 방법을 이용한 확장성에 대한 연구가 이루어졌다. 그러나, LDUP 방법은 2D 그리드의 크기를 조작하는데 한계가 있어, 패치 사이의 거리가 드물게 패킹되고, 이는 압축 효율을 떨어뜨린다. 본 논문에서는 이러한 단점을 극복하기 위해 양방향 패치 패킹을 활용한 LoD 제어 테이블 기반 밀도 확장성(LoD control table based Density scalability by using Bidirectional Patch packing, LDBP) 방식을 제안한다. 제안된 LDBP 방법은 패치가 패킹된 영상에서 빈 공간을 효과적으로 감소시켰으며, 압축 효율 측면에서 LDUP 방법에 비해 더 높은 BD-Rate 이점을 얻었다. 제안된 LDBP 방법은 3D 포인트 클라우드 압축 시 포인트 클라우드 밀도 확장성을 기존의 LDUP 보다 효과적으로 달성하였다.