Name
Abstract | ||
|---|---|---|
We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view. As for synthesis in the 3D space, we implement a cascaded network architecture with two hourglass modules to generate point-wise coarse and fine features from semantics and per-class latent vectors, followed by projection to frames and an upsampling module to obtain the final realistic video. By leveraging computed correspondences, the produced street-view video frames adhere to the 3D geometric scene structure and maintain temporal consistency. Qualitative and quantitative experiments demonstrate superior results compared to other state-of-the-art synthesis approaches that either lack temporal consistency or realistic appearance. To the best of our knowledge, our work is the first one to synthesize cross-view images to videos. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCV48922.2021.01221 | ICCV |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuoyue Li | 1 | 0 | 0.34 |
| Zhenqiang Li | 2 | 0 | 0.34 |
| Zhaopeng Cui | 3 | 93 | 16.66 |
| Qin, R. | 4 | 38 | 8.77 |
| Marc Pollefeys | 5 | 7671 | 475.90 |
| Martin R. Oswald | 6 | 54 | 13.44 |