Name
Abstract | ||
|---|---|---|
Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas. However, these panoramas are only available at sparse intervals along the path they are taken, resulting in visual discontinuities during navigation. Prior art in view synthesis is usually built upon a set of perspective images, a pair of stereoscopic images, or a monocular image, but barely examines wide-baseline panoramas, which are widely adopted in commercial platforms to optimize bandwidth and storage usage. In this paper, we leverage the unique characteristics of wide-baseline panoramas and present OmniSyn, a novel pipeline for 360 degrees view synthesis between wide-baseline panoramas. OmniSyn predicts omnidirectional depth maps using a spherical cost volume and a monocular skip connection, renders meshes to 360 degrees images, and synthesizes intermediate views with a fusion network. We envision our work may inspire future research for this unheeded real-world task and eventually produce a smoother experience for navigating immersive maps. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/VRW55335.2022.00186 | 2022 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS (VRW 2022) |
Keywords | DocType | Citations |
Computing methodologies, Computer graphics, Image manipulation, Image-based rendering | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Li | 1 | 147 | 17.08 |
| Yinda Zhang | 2 | 350 | 24.48 |
| Christian Hane | 3 | 281 | 17.03 |
| Danhang Tang | 4 | 0 | 0.68 |
| Amitabh Varshney | 5 | 1704 | 172.25 |
| Ruofei Du | 6 | 0 | 0.34 |