Monocular depth estimation based on deep learning: An overview
Sign Up to like & getrecommendations! 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
An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation
Sign Up to like & getrecommendations! 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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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
Lightweight Monocular Depth Estimation on Edge Devices
Sign Up to like & getrecommendations! 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
Unsupervised Learning of Monocular Depth and Ego-Motion in Outdoor/Indoor Environments
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DOI: 10.1109/jiot.2022.3151629
Abstract: Visual-based unsupervised learning [1]–[3] has emerged as a promising approach in estimating monocular depth and ego-motion, avoiding intensive efforts on collecting and labeling the ground truth. However, they are still restrained by the brightness constancy… read more here.
Keywords: ego motion; monocular depth; depth ego; depth ... See more keywords
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3210877
Abstract: Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially… read more here.
Keywords: self supervised; scale invariant; scale; monocular depth ... See more keywords
LD-Net: A Lightweight Network for Real-Time Self-Supervised Monocular Depth Estimation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3160656
Abstract: Self-supervised monocular depth estimation from video sequences is promising for 3D environments perception. However, most existing methods use complicated depth networks to realize monocular depth estimation, which are often difficultly applied to resource-constrained devices. To… read more here.
Keywords: self supervised; monocular depth; depth estimation; supervised monocular ... See more keywords
RAFM: Recurrent Atrous Feature Modulation for Accurate Monocular Depth Estimating
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3189597
Abstract: Current top-performing coarse-to-fine monocular depth estimation systems mainly depend on deeper backbones, such as a full ResNet50. These systems benefit from the powerful multiscale feature representations but suffer from the high computational costs and memory… read more here.
Keywords: recurrent atrous; rafm recurrent; depth; atrous feature ... See more keywords
Monocular Depth Estimation With Augmented Ordinal Depth Relationships
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2019.2929202
Abstract: Most existing algorithms for depth estimation from single monocular images need large quantities of metric ground-truth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and… read more here.
Keywords: estimation augmented; depth; monocular depth; ground truth ... See more keywords
CORNet: Context-Based Ordinal Regression Network for Monocular Depth Estimation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2021.3128505
Abstract: Monocular depth estimation, as one of the fundamental tasks of computer vision, plays a crucial role in three-dimensional (3D) scene understanding and perception. Usually, deep learning methods recover monocular depth maps using continuous regression manners… read more here.
Keywords: depth; depth maps; ordinal regression; monocular depth ... See more keywords
Unsupervised Monocular Depth Estimation via Recursive Stereo Distillation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2021.3072215
Abstract: Existing unsupervised monocular depth estimation methods resort to stereo image pairs instead of ground-truth depth maps as supervision to predict scene depth. Constrained by the type of monocular input in testing phase, they fail to… read more here.
Keywords: stereo; depth; estimation; monocular depth ... See more keywords