Image-quality Improvement of Omnidirectional Free-viewpoint Images by Generative Adversarial Networks
Oto Takeuchi, Hidehiko Shishido, Yoshinari Kameda, Hansung Kim, Itaru Kitahara · 2020
This paper proposes a method to improve the quality of omnidirectional free-viewpoint images using generative adversarial networks (GAN). By estimating the 3D information of the capturing space while integrating the omnidirectional images taken from multiple viewpoints, it is possible to generate an arbitrary omnidirectional appearance. However, the image quality of free-viewpoint images deteriorates due to artifacts caused by 3D estimation errors and occlusion. We solve this problem by using GAN and, moreover, by focusing on projective geometry during training, we further improve image quality by converting the omnidirectional image into perspective-projection images.