Stereo Generation from a Single Image Using Deep Residual Network
PubDate: September 2018
Teams: Beijing Institute of Technology
Writers: Jun Huang; Tianteng Bi; Yue Liu; Yongtian Wang
In this paper, we propose a framework to generate stereoscopic content from a single image using the relative depth label predicted from deep residual network. Specifically, our framework first obtains a coarse relative depth label from the network and refines it to painting depth by sampling and interpolation, then an unsupervised clustering algorithm is employed to separate pixels of different depths into different layers to generate stereoscopic images. Experimental results with good visual effects demonstrate that the proposed method can be generally applied in both outdoor and indoor scenes. Meanwhile the quantitative results on relative depth estimation from a single image are comparable to state-of-the-art. Further experiments show the application possibility of our method in VR and panorama.