Nguyen, P;
Karnewar, A;
Huynh, L;
Rahtu, E;
Matas, J;
Heikkila, J;
(2022)
RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis.
In:
2021 International Conference on 3D Vision (3DV).
(pp. pp. 1095-1105).
IEEE: London, United Kingdom.
Preview |
PDF
rgbd_net_paper.pdf - Accepted Version Download (11MB) | Preview |
Abstract
We propose a new cascaded architecture for novel view synthesis,called RGBD-Net,which consists of two core components: a hierarchical depth regression network and a depth-aware generator network. The former one predicts depth maps of the target views by using adaptive depth scaling,while the latter one leverages the predicted depths and renders spatially and temporally consistent target images. In the experimental evaluation on standard datasets,RGBD-Net not only outperforms the state-of-the-art by a clear margin,but it also generalizes well to new scenes without per-scene optimization. Moreover,we show that RGBD-Net can be optionally trained without depth supervision while still retaining high-quality rendering. Thanks to the depth regression network,RGBD-Net can be also used for creating dense 3D point clouds that are more accurate than those produced by some state-of-the-art multi-view stereo methods.
| Type: | Proceedings paper |
|---|---|
| Title: | RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis |
| Event: | 2021 International Conference on 3D Vision (3DV) |
| Dates: | 1 Dec 2021 - 3 Dec 2021 |
| ISBN-13: | 9781665426886 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/3DV53792.2021.00117 |
| Publisher version: | https://doi.org/10.1109/3DV53792.2021.00117 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Point cloud compression, Three-dimensional displays, Adaptive systems, Image color analysis, Color, Rendering (computer graphics), Cameras |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146365 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
