Open Access
ARTICLE
Shadow Detection and Removal From Photo-Realistic Synthetic Urban Image Using Deep Learning
Hee-Jin Yoon1, Kang-Jik Kim1, Jun-Chul Chun1,*
1 Department of Computer Science, Kyonggi University, Gyeonggi-do, 16227, Korea.
* Corresponding Author: Jun-Chul Chun. Email: .
Computers, Materials & Continua 2020, 62(1), 459-472. https://doi.org/10.32604/cmc.2020.08799
Abstract
Recently, virtual reality technology that can interact with various data is used
for urban design and analysis. Reality, one of the most important elements in virtual
reality technology, means visual expression so that a person can experience threedimensional
space like reality. To obtain this realism, real-world data are used in the
various fields. For example, in order to increase the realism of 3D modeled building
textures real aerial images are utilized in 3D modelling. However, the aerial image
captured during the day can be shadowed by the sun and it can cause the distortion or
deterioration of image. To resolve this problem, researches on detecting and removing
shadows have been conducted, but the detecting and removing shadow is still considered
as a challenging problem. In this paper, we propose a novel method for detecting and
removing shadows using deep learning. For this work, we first a build a new dataset of
photo-realistic synthetic urban data based on the virtual environment using 3D spatial
information provided by VWORLD. For detecting and removing shadow from the dataset,
firstly, the 1-channel shadow mask image is inferred from the 3-channel shadow image
through the CNN. Then, to generate a shadow-free image, a 3-channel shadow image and
a detected 1-channel shadow mask into the GAN is executed. From the experiments, we
can prove that the proposed method outperforms the existing methods in detecting and
removing shadow.
Keywords
Cite This Article
H. Yoon, K. Kim and J. Chun, "Shadow detection and removal from photo-realistic synthetic urban image using deep learning,"
Computers, Materials & Continua, vol. 62, no.1, pp. 459–472, 2020. https://doi.org/10.32604/cmc.2020.08799
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