RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00357172" target="_blank" >RIV/68407700:21230/21:00357172 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/3DV53792.2021.00117" target="_blank" >https://doi.org/10.1109/3DV53792.2021.00117</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/3DV53792.2021.00117" target="_blank" >10.1109/3DV53792.2021.00117</a>
Alternative languages
Result language
angličtina
Original language name
RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
3DV 2021: Proceedings of the International Conference on 3D Vision
ISBN
978-1-6654-2688-6
ISSN
2378-3826
e-ISSN
2475-7888
Number of pages
11
Pages from-to
1095-1105
Publisher name
IEEE Computer Soc.
Place of publication
Los Alamitos, CA
Event location
Virtual
Event date
Dec 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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