3차원 포인트 클라우드 데이터를 활용한 객체 탐지 기법인 PointNet과 RandLA-Net

이동건; 지승환; 박본영

doi:10.3744/SNAK.2022.59.5.330

주제분류

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저널정보

대한조선학회
대한조선학회 논문집 학술저널
대한조선학회논문집 제59권 제5호(통권 제245호)
2022.10 330 - 337 (8page)
DOI : 10.3744/SNAK.2022.59.5.330

저자정보

이동건 (목포해양대학교)
지승환 (목포해양대학교)
박본영 (목포해양대학교)

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초록·키워드

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Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet"s performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

#Deep learning(딥러닝) #Point clouds(포인트 클라우드) #Object detection(객체 탐지) #PointNet(포인트넷) #RandLA-Net(랜들라넷)