Fusion of an RGB camera and LiDAR sensor through a Graph CNN for 3D object detectionopen access
- Authors
- Choi, Jinsol; Shin, Minwoo; Paik, 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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- There are no files associated with this item.
- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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