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Fusion of an RGB camera and LiDAR sensor through a Graph CNN for 3D object detectionopen access

Authors
Choi, JinsolShin, MinwooPaik, Joonki
Issue Date
May-2023
Publisher
Optica Publishing Group
Citation
OPTICS CONTINUUM, v.2, no.5, pp 1166 - 1179
Pages
14
Journal Title
OPTICS CONTINUUM
Volume
2
Number
5
Start Page
1166
End Page
1179
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67260
DOI
10.1364/OPTCON.479777
ISSN
2770-0208
2770-0208
Abstract
Despite the recent development of RGB camera and LiDAR sensor fusion technology using deep learning, fusion without loss of information is a very difficult problem because the structural data characteristics of the two sensors are different. To solve this problem, we use a graph convolutional neural network (Graph CNN) to fuse RGB and LiDAR sensors. The proposed method creates a fusion feature by supplementing the geometric information of each feature in the process of fusing the features of two different sensors. Based on the experimental data, the proposed method has higher accuracy in detecting distant objects and complex situations than the existing method. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
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Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles

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Paik, Joon Ki
첨단영상대학원 (영상학과)
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