EVALUATING MONOCULAR DEPTH ESTIMATION METHODS
Abstract. Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including applications in simultaneous localization and mapping (SLAM), scene comprehension, 3D modeling, robotics, and autonomous driving. Depth information retrieval becomes especially crucial in situations where other sources like stereo images, optical flow, or point clouds are not available. In contrast to traditional stereo or multi-view methods, MDE techniques require fewer computational resources and smaller datasets. This research work presents a comprehensive analysis and evaluation of some state-of-the-art MDE methods, considering their ability to infer depth information in terrestrial images. The evaluation includes quantitative assessments using ground truth data, including 3D analyses and inference time.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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EVALUATING MONOCULAR DEPTH ESTIMATION METHODS ; volume:XLVIII-1/W3-2023 ; year:2023 ; pages:137-144 ; extent:8
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-1/W3-2023 (2023), 137-144 (gesamt 8)
- Classification
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Informatik
- Creator
- DOI
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10.5194/isprs-archives-XLVIII-1-W3-2023-137-2023
- URN
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urn:nbn:de:101:1-2023102604414927874856
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:57 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Padkan, N.
- Trybala, P.
- Battisti, R.
- Remondino, Fabio
- Bergeret, C.
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High quality monocular depth estimation with parallel decoder
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
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MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS
MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES
High quality monocular depth estimation with parallel decoder
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
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