Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354697" target="_blank" >RIV/68407700:21230/21:00354697 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCV48922.2021.01253" target="_blank" >https://doi.org/10.1109/ICCV48922.2021.01253</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV48922.2021.01253" target="_blank" >10.1109/ICCV48922.2021.01253</a>
Alternative languages
Result language
angličtina
Original language name
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion
Original language description
In this paper, we propose enhancing monocular depthestimation by adding 3D points as depth guidance. Un-like existing depth completion methods, our approach per-forms well on extremely sparse and unevenly distributedpoint clouds, which makes it agnostic to the source of the3D points. We achieve this by introducing a novel multi-scale 3D point fusion network that is both lightweight andefficient. We demonstrate its versatility on two differentdepth estimation problems where the 3D points have beenacquired with conventional structure-from-motion and Li-DAR. In both cases, our network performs on par with state-of-the-art depth completion methods and achieves signifi-cantly higher accuracy when only a small number of pointsis used while being more compact in terms of the num-ber of parameters. We show that our method outperformssome contemporary deep learning based multi-view stereoand structure-from-motion methods both in accuracy and incompactness.
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
ICCV2021: Proceedings of the International Conference on Computer Vision
ISBN
978-1-6654-2812-5
ISSN
1550-5499
e-ISSN
2380-7504
Number of pages
10
Pages from-to
12767-12776
Publisher name
IEEE
Place of publication
Piscataway
Event location
Montreal
Event date
Oct 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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