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   Articles with "depth estimation" as a keyword



Depth estimation improvement in 3D integral imaging using an edge removal approach

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recommendations! Published in 2018 at "Pattern Analysis and Applications"

DOI: 10.1007/s10044-018-0721-4

Abstract: AbstractA new depth estimation method for 3D reconstruction in a synthetic aperture integral imaging framework is presented. This method removes the edges of the objects in the elemental images when the objects are in focus.… read more here.

Keywords: estimation improvement; integral imaging; depth estimation; improvement integral ... See more keywords

Self-supervised Monocular Trained Depth Estimation Using Triplet Attention and Funnel Activation

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recommendations! Published in 2021 at "Neural Processing Letters"

DOI: 10.1007/s11063-021-10608-5

Abstract: Dense depth estimation based on a single image is a basic problem in computer vision and has exciting applications in many robotic tasks. Modelling fully supervised methods requires the acquisition of accurate and large ground… read more here.

Keywords: self supervised; estimation; depth estimation; triplet attention ... See more keywords

Monocular depth estimation based on deep learning: An overview

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recommendations! Published in 2020 at "Science China Technological Sciences"

DOI: 10.1007/s11431-020-1582-8

Abstract: Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints.… read more here.

Keywords: depth; monocular depth; depth estimation; deep learning ... See more keywords
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Real-to-virtual domain transfer-based depth estimation for real-time 3D annotation in transnasal surgery: a study of annotation accuracy and stability

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recommendations! Published in 2021 at "International Journal of Computer Assisted Radiology and Surgery"

DOI: 10.1007/s11548-021-02346-9

Abstract: Surgical annotation promotes effective communication between medical personnel during surgical procedures. However, existing approaches to 2D annotations are mostly static with respect to a display. In this work, we propose a method to achieve 3D… read more here.

Keywords: surgery; depth estimation; accuracy; annotation ... See more keywords
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Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach

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recommendations! Published in 2021 at "Remote Sensing of Environment"

DOI: 10.1016/j.rse.2021.112630

Abstract: Abstract Snow cover is highly critical for global water and energy cycles because of its wide areal extent, high reflectivity and good thermal insulation. Knowledge of snow conditions, e.g., snow water equivalent (SWE) and snow… read more here.

Keywords: snow; approach; model; snow depth ... See more keywords

AMENet is a monocular depth estimation network designed for automatic stereoscopic display

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-56095-1

Abstract: Monocular depth estimation has a wide range of applications in the field of autostereoscopic displays, while accuracy and robustness in complex scenes are still a challenge. In this paper, we propose a depth estimation network… read more here.

Keywords: depth estimation; monocular depth; estimation; estimation network ... See more keywords

Dense monocular depth estimation for stereoscopic vision based on pyramid transformer and multi-scale feature fusion

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-57908-z

Abstract: Stereoscopic display technology plays a significant role in industries, such as film, television and autonomous driving. The accuracy of depth estimation is crucial for achieving high-quality and realistic stereoscopic display effects. In addressing the inherent… read more here.

Keywords: depth estimation; pyramid transformer; depth; estimation stereoscopic ... See more keywords

Efficient attention vision transformers for monocular depth estimation on resource-limited hardware

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-06112-8

Abstract: Vision Transformers show important results in the current Deep Learning technological landscape, being able to approach complex and dense tasks, for instance, Monocular Depth Estimation. However, in the transformer architecture, the attention module introduces a… read more here.

Keywords: depth estimation; vision transformers; monocular depth; efficient attention ... See more keywords

Monocular depth estimation via a detail semantic collaborative network for indoor scenes

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-96024-4

Abstract: Monocular image depth estimation is crucial for indoor scene reconstruction, and it plays a significant role in optimizing building energy efficiency, indoor environment modeling, and smart space design. However, the small depth variability of indoor… read more here.

Keywords: depth estimation; detail semantic; indoor scenes; semantic collaborative ... See more keywords

Enhanced blur‐robust monocular depth estimation via self‐supervised learning

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recommendations! Published in 2024 at "Electronics Letters"

DOI: 10.1049/ell2.70098

Abstract: This letter presents a novel self‐supervised learning strategy to improve the robustness of a monocular depth estimation (MDE) network against motion blur. Motion blur, a common problem in real‐world applications like autonomous driving and scene reconstruction,… read more here.

Keywords: depth estimation; self supervised; supervised learning; blur ... See more keywords

An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

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recommendations! Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2946323

Abstract: Depth estimation from a single image plays an important role in 3D scene perception. Owing to the development of deep convolutional neural networks (CNNs), monocular depth estimation models have achieved a large number of exciting… read more here.

Keywords: depth; model; monocular depth; adaptive unsupervised ... See more keywords