Name
Abstract | ||
|---|---|---|
In this paper, we present a novel view synthesis method named Visto, which uses a reference input view to generate synthesized views in nearby viewpoints. We formulate the problem as a joint optimization of inter-view texture and depth map similarity, a framework that is significantly different from other traditional approaches. As such, Visto tends to implicitly inherit the image characteristics from the reference view without the explicit use of image priors or texture modeling. Visto assumes that each patch is available in both the synthesized and reference views and thus can be applied to the common area between the two views but not the out-of-region area at the border of the synthesized view. Visto uses a Gauss-Seidel-like iterative approach to minimize the energy function. Simulation results suggest that Visto can generate seamless virtual views and outperform other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2013.2289994 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
view synthesis,non-local,seamless virtual views,interview texture,joint optimization,gauss-seidel-like,visto,gauss-seidel-like iterative approach,energy function minimization,seamless view synthesis,texture optimization,minimisation,image texture,reference input view,iterative methods,depth map similarity | Computer vision,Pattern recognition,Viewpoints,Image texture,Iterative method,Computer science,Common area,View synthesis,Minimisation (psychology),Artificial intelligence,Depth map,Prior probability | Journal |
Volume | Issue | ISSN |
23 | 1 | 1941-0042 |
Citations | PageRank | References |
3 | 0.39 | 31 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxiu Sun | 1 | 160 | 20.79 |
| Oscar C. Au | 2 | 1592 | 176.54 |
| Lingfeng Xu | 3 | 53 | 9.81 |
| Yujun Li | 4 | 104 | 18.20 |
| Wei Hu | 5 | 244 | 22.01 |