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   Articles with "monocular depth" as a keyword



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

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

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

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

Leveraging Contextual Information for Monocular Depth Estimation

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3016008

Abstract: Humans strongly rely on visual cues to understand scenes such as segmenting, detecting objects, or measuring the distance from nearby objects. Recent studies suggest that deep neural networks can take advantage of contextual representation for… read more here.

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

Eite-Mono: An Extreme Lightweight Architecture for Self-Supervised Monocular Depth Estimation

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3432181

Abstract: In intelligent mine construction, depth prediction via machine vision plays a pivotal role in enhancing visual perception. This need, coupled with the scarcity of high-quality monocular depth estimation datasets, has led to the development of… read more here.

Keywords: depth estimation; self supervised; monocular depth; depth ... See more keywords

Lightweight Self-Supervised Monocular Depth Estimation Through CNN and Transformer Integration

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3494872

Abstract: Self-supervised monocular depth estimation is a promising research area due to its ability to train models without relying on expensive and difficult-to-obtain ground truth depth labels. In this domain, models often employ Convolutional Neural Networks… read more here.

Keywords: depth estimation; self supervised; monocular depth; depth ... See more keywords

Contrastive Feature Bin Loss for Monocular Depth Estimation

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3551435

Abstract: Recently monocular depth estimation has achieved notable performance using encoder-decoder-based models. These models have utilized the Scale-Invariant Logarithmic (SILog) loss for effective training, leading to significant performance improvements. However, since the SILog loss is designed… read more here.

Keywords: depth estimation; performance; monocular depth; loss ... See more keywords

Image Intrinsic-Based Unsupervised Monocular Depth Estimation in Endoscopy.

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recommendations! Published in 2024 at "IEEE journal of biomedical and health informatics"

DOI: 10.1109/jbhi.2024.3400804

Abstract: Unsupervised monocular depth estimation plays a vital role for endoscopy-based minimally invasive surgery (MIS). However, it remains challenging due to the distinctive imaging characteristics of endoscopy which disrupt the assumption of photometric consistency, a foundation… read more here.

Keywords: image; monocular depth; unsupervised monocular; depth estimation ... See more keywords

MonoLoT: Self-Supervised Monocular Depth Estimation in Low-Texture Scenes for Automatic Robotic Endoscopy

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recommendations! Published in 2024 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2024.3423791

Abstract: The self-supervised monocular depth estimation framework is well-suited for medical images that lack ground-truth depth, such as those from digestive endoscopes, facilitating navigation and 3D reconstruction in the gastrointestinal tract. However, this framework faces several… read more here.

Keywords: depth estimation; monocular depth; depth; self supervised ... See more keywords

Lightweight Monocular Depth Estimation on Edge Devices

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recommendations! Published in 2022 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2022.3151374

Abstract: Given monocular images as inputs, monocular depth estimation (MDE) infers pixel-level depth. MDE is always a critical stage in scene sensing on edge devices. Existing MDE studies frequently employ deep neural networks (DNNs) for MDE,… read more here.

Keywords: edge devices; depth; depth estimation; monocular depth ... See more keywords