- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
High quality monocular depth estimation with parallel decoder ; volume:12 ; number:1 ; day:5 ; month:10 ; year:2022 ; pages:1-13 ; date:12.2022
Scientific reports ; 12, Heft 1 (5.10.2022), 1-13, 12.2022
- Creator
-
Liu, Jiatao
Zhang, Yaping
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-022-20909-x
- URN
-
urn:nbn:de:101:1-2022122221034746472273
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:28 AM CEST
Data provider
This object is provided by:
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Associated
- Liu, Jiatao
- Zhang, Yaping
- SpringerLink (Online service)
Other Objects (12)
EVALUATING MONOCULAR DEPTH ESTIMATION METHODS
MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS
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CFDepthNet: Monocular Depth Estimation Introducing Coordinate Attention and Texture Features
What makes the unsupervised monocular depth estimation (UMDE) model training better
EVALUATING MONOCULAR DEPTH ESTIMATION METHODS
MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS
MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES
PCTDepth: Exploiting Parallel CNNs and Transformer via Dual Attention for Monocular Depth Estimation
Learning-based dense monocular depth estimation for cystoscopic videos
Lightweight monocular depth estimation using a fusion-improved transformer
Monocular depth sensing using metalens
DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation
Monocular depth estimation with edge-based constraints using active learning optimization
Monocular Depth Estimation with Edge-Based Constraints using Active Learning Optimization
CFDepthNet: Monocular Depth Estimation Introducing Coordinate Attention and Texture Features
What makes the unsupervised monocular depth estimation (UMDE) model training better
EVALUATING MONOCULAR DEPTH ESTIMATION METHODS
MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS
MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES
PCTDepth: Exploiting Parallel CNNs and Transformer via Dual Attention for Monocular Depth Estimation
Learning-based dense monocular depth estimation for cystoscopic videos
Lightweight monocular depth estimation using a fusion-improved transformer
Monocular depth sensing using metalens
DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation
Monocular depth estimation with edge-based constraints using active learning optimization
Monocular Depth Estimation with Edge-Based Constraints using Active Learning Optimization
CFDepthNet: Monocular Depth Estimation Introducing Coordinate Attention and Texture Features