Work

Generation of Stereo Images Based on a View Synthesis Network

Year: 2020

Type: article

Abstract: The conventional warping method only considers translations of pixels to generate stereo images. In this paper, we propose a model that can generate stereo images from a single image, considering both translation as well as rotation of objects in the image. We modified the appearance flow network to make it more general and suitable for our model. We also used a reference image to improve the inpainting method. The quality of images resulting from our model is better than that of images generated using conventional warping. Our model also better retained the structure of objects in the input image. In addition, our model does not limit the size of the input image. Most importantly, because our model considers the rotation of objects, the resulting images appear more stereoscopic when viewed with a device.

Source: Applied Sciences

DOI: 10.3390/app10093101

Authors: Yuan-Mau Lo, Chin-Chen Chang, Der-Lor Way, Zen-Chung Shih

Institutions: National Yang Ming Chiao Tung University, National United University, Taipei National University of the Arts

Language: English

Topic: Advanced Vision and Imaging

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,901

Funder: Ministry of Science and Technology, Taiwan