Joint Inpainting of RGB and Depth Images by Generative Adversarial Network with a Late Fusion Approach
Ryo Fujii, Ryo Hachiuma, Hideo Saitô · 2019
Image inpainting aims to restore texture of missing regions in scene from an RGB image. In this paper, we aim to restore not only the texture but also the geometry of the missing regions in scene from a pair of RGB and depth images. Inspired by the recent development of generative adversarial network, we employ an encoder-decoderbased generative adversarial network with the input of RGB and depth image. The experimental results show that our method restores the missing region of both RGB and depth image.