標題: | Generation of Stereo Images Based on a View Synthesis Network |
作者: | Lo, Yuan-Mau Chang, Chin-Chen Way, Der-Lor Shih, Zen-Chung 多媒體工程研究所 Institute of Multimedia Engineering |
關鍵字: | stereo images;view synthesis;neural network;semantic segmentation;depth estimation |
公開日期: | 1-五月-2020 |
摘要: | 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. |
URI: | http://dx.doi.org/10.3390/app10093101 http://hdl.handle.net/11536/154572 |
DOI: | 10.3390/app10093101 |
期刊: | APPLIED SCIENCES-BASEL |
Volume: | 10 |
Issue: | 9 |
起始頁: | 0 |
結束頁: | 0 |
顯示於類別: | 期刊論文 |