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http://dx.doi.org/10.14372/IEMEK.2019.14.1.1

Deep Learning Based Gray Image Generation from 3D LiDAR Reflection Intensity  

Kim, Hyun-Koo (Yeungnam University)
Yoo, Kook-Yeol (Yeungnam University)
Park, Ju H. (Yeungnam University)
Jung, Ho-Youl (Yeungnam University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.14, no.1, 2019 , pp. 1-9 More about this Journal
Abstract
In this paper, we propose a method of generating a 2D gray image from LiDAR 3D reflection intensity. The proposed method uses the Fully Convolutional Network (FCN) to generate the gray image from 2D reflection intensity which is projected from LiDAR 3D intensity. Both encoder and decoder of FCN are configured with several convolution blocks in the symmetric fashion. Each convolution block consists of a convolution layer with $3{\times}3$ filter, batch normalization layer and activation function. The performance of the proposed method architecture is empirically evaluated by varying depths of convolution blocks. The well-known KITTI data set for various scenarios is used for training and performance evaluation. The simulation results show that the proposed method produces the improvements of 8.56 dB in peak signal-to-noise ratio and 0.33 in structural similarity index measure compared with conventional interpolation methods such as inverse distance weighted and nearest neighbor. The proposed method can be possibly used as an assistance tool in the night-time driving system for autonomous vehicles.
Keywords
Artificial intelligence; Deep learning; Fully convolution network; Image generation; LiDAR sensor;
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Times Cited By KSCI : 2  (Citation Analysis)
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