• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.692-703
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• 2021

Recently, with the development of computer graphics technology, research on technology for expressing real objects as more realistic virtual graphics is being actively conducted. Point cloud is a technology that uses numerous points, including 2D spatial coordinates and color information, to represent 3D objects, and they require huge data storage and high-performance computing devices to provide various services. Video-based Point Cloud Compression (V-PCC) technology is currently being studied by the international standard organization MPEG, which is a projection based method that projects point cloud into 2D plane, and then compresses them using 2D video codecs. V-PCC technology compresses point cloud objects using 2D images such as Occupancy map, Geometry image, Attribute image, and other auxiliary information that includes the relationship between 2D plane and 3D space. When increasing the density of point cloud or expanding an object, 3D calculation is generally used, but there are limitations in that the calculation method is complicated, requires a lot of time, and it is difficult to determine the correct location of a new point. This paper proposes a method to generate additional points at more accurate locations with less computation by applying 2D interpolation to the image on which the point cloud is projected, in the V-PCC technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.277-278
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• 2022

본 연구는 3차원 포인트클라우드로부터 단면 정보를 자동으로 추출할 수 있는 알고리즘에 관한 것이다. 3차원 스캐너로부터 획득한 포인트클라우드 데이터는 다양한 제조 공정의 결과물인 산업 제품의 접합 상태를 파악하는데 자주 사용된다. 하지만 많은 노이즈를 포함하는 포인트클라우드 데이터로부터 제조 상태에 대한 수치적인 결과를 반복적으로 획득하기에는 많은 비용이 수반된다. 따라서 본 연구는 산업 제품의 접합부에 대한 포인트클라우드로부터 단면 정보를 자동으로 추출할 수 있는 알고리즘을 소개하고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.417-418
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• 2012

본 논문에서는 3차원 데이터를 이용한 효율적인 실내 공간 표현 기법을 제안한다. 제안하는 기법은 3차원 데이터의 획득과 실내 구조 및 영상 정보를 표현하기 위한 표현 복원으로 구성된다. 3차원 데이터는 레이저 거리 측정기(laser range finder, LRF)와 전방향(omni) 카메라를 통해 획득한 포인트 클라우드 공간 정보와 전방향 텍스쳐 영상으로 구성된다. 실내 구조를 복원하기 위해, 획득한 포인트 클라우드를 복셀 격자 기반의 샘플링 기법을 통해 균일화하고 포아송 표면 재구성(Poisson surface rocoostruction) 기법을 통해 3차원 메쉬를 생성한다. 그리고 전방향 텍스쳐 영상과 3차원 메쉬외 기하학적 관계를 이용한 텍스쳐 매핑 기법을 통해 최종적으로 3차원 메쉬 표면을 복원한다. 실험 결과를 통해 제안하는 기법이 실내 공간을 효과적으로 표현함을 확인한다.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.323-324
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• 2023

본 논문에서는 다양한 복원 인공지능 알고리즘 중 하나인 3차원 복원 기술은 실제로 존재하는 물체의 2차원적인 픽셀을 3차원의 형태로 구현하여 형상화한다. 정확한 3차원 정보 처리가 요구됨에 따라 포인트 클라우드로 표현되는 데이터를 통해 정확한 쿨체의 크기 정보나 좌표 정보를 표시할 수 있다. 데이터의 픽셀을 분석하여 3차원의 형태로 구현할 것을 정의하는 복셀화(Voxelization) 알고리즘 전처리 과정을 통해 3차원 복원 기술 3D-GAN 활용으로 3차원 형태 형상화를 하였다. 본 논문에서는 3차원 복원 알고리즘 통하여 2차원 포인트 클라우드를 분석해 3차원 형태로 복원하는 기술에 대한 설명한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.279-282
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• 2021

포인트 클라우드는 3 차원 물체를 표현하기 위한 점들의 집합으로, 동적인 3 차원 데이터를 정밀하게 획득할 수 있기에 이의 효율적인 압축의 필요성이 대두되고 있다. 기존 3D DCT(3D Discrete Cosine Transform)를 이용한 동적 객체의 포인트 클라우드 압축 방식은 Inter 프레임 압축을 고려하지 않아 압축시의 데이터 압축률에 한계가 있다. 따라서 본 논문은 이러한 문제점을 개선하기 위해 3D DCT 를 이용한 움직임 예측을 통하여 포인트 클라우드 영상의 I 프레임 및 P 프레임을 압축하는 방식을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.16-18
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• 2021

미디어 기술은 사용자가 더욱 몰입감을 느낄 수 있는 방향으로 개발되어 왔다. 이러한 흐름에 따라 기존의 2D 이미지에 비해 깊이감을 느낄 수 있는 증강 현실, 가상 현실 등 3D 공간 데이터를 활용하는 미디어가 주목을 받고 있다. 포인트 클라우드는 수많은 3차원 좌표를 가진 여러 개의 점들로 구성된 데이터 형식이므로 각각의 점들에 대한 좌표 및 색상 정보를 사용하여 3D 미디어를 표현한다. 고정된 크기의 해상도를 갖는 2D 이미지와 다르게 포인트 클라우드는 포인트의 개수에 따라 용량이 유동적이며, 이를 기존의 비디오 코덱을 사용하여 압축하기 위해 국제 표준기구인 MPEG(Moving Picture Experts Group)에서는 Video-based Point Cloud Compression (V-PCC)을 제정하였다. V-PCC는 3D 포인트 클라우드 데이터를 직교 평면 벡터를 이용하여 2D 패치로 분해하고 이러한 패치를 2D 이미지에 배치한 다음 기존의 2D 비디오 코덱을 사용하여 압축한다. 본 논문에서는 앞서 설명한 2D 패치 이미지에 super resolution network를 적용함으로써 3D 포인트 클라우드의 성능 향상하는 방안을 제안한다.

• 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.

• 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.