Name
Abstract | ||
|---|---|---|
In this paper a plane-based backward warping algorithm is proposed to generate novel views from multiple reference images. First, depth information is employed to reconstruct space planes from individual reference images and calculate the potential occluding relationship between these planes. Then the planes which represent each identical space plane from different reference images are compared with each other to decide the one with the best sample rate to be preserved and used in the later warping period while the other samples are abandoned. While the image of a novel view is produced, traditional methods in computer graphics, such as visibility test and clipping, are used to process the planes reconstructed. Then the planes processed are projected onto the desired image from the knowledge on which plane the desired image pixels are warped from can be acquired. Finally, pixels' depth of the desired image is calculated and then a backward warping is performed from these pixels to the reference images to obtain their colors. The storage requirement in the algorithm is small and increases slowly with the number of reference images increases. By combining the strategy of only preserving the best sample parts and the backward warping algorithm, the sample problem could be well tackled. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/BF02946650 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
multiple reference image,individual reference image,novel view,warping period,reference images increase,different reference image,reference image,image pixel,best sample part,warping algorithm,sample rate,image based rendering,computer graphic | Computer vision,Visibility,Image warping,Computer graphics (images),Computer science,Sampling (signal processing),Pixel,Artificial intelligence,Image-based modeling and rendering,Computer graphics | Journal |
Volume | Issue | ISSN |
18 | 1 | 1860-4749 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| YanCi Zhang | 1 | 113 | 14.96 |
| Xue-Hui Liu | 2 | 256 | 26.39 |
| Enhua Wu | 3 | 916 | 115.33 |
| 张严辞 | 4 | 1 | 0.36 |
| 刘学慧 | 5 | 1 | 0.36 |
| 吴恩华 | 6 | 1 | 1.04 |