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Multi-scale Context Attention Network for Stereo Matching
2019
IEEE Access
Preserved Fulltext
Recently, many works for stereo matching with convolutional neural networks have gained satisfactory performance. ...
Additionally, attention mechanism is used to pick out informative features for disparity refinement sub-network. ...
features for stereo matching. ...
doi:10.1109/access.2019.2895271
fatcat:qukrymhzx5at7kgrdr5sfoxmba
Parallax‐based second‐order mixed attention for stereo image super‐resolution
2021
IET Computer Vision
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To address this issue, in this work, a parallax-based second-order mixed attention stereo SR network (PSMASSRnet) is proposed to integrate the cross-view information from a stereo image pair for SR. ...
Most recent methods use the attention mechanism to get stereo correspondence. ...
| Dense cross ASPP block Dense cross ASPP block is used in our network to detect multi-scale information. ...
doi:10.1049/cvi2.12063
fatcat:mghofhx75zdu3cawc5wxlw3ufy
Hierarchical Neural Architecture Search for Deep Stereo Matching
[article]
2020
arXiv
pre-print
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Specifically, following the gold standard pipeline for deep stereo matching (i.e., feature extraction -- feature volume construction and dense matching), we optimize the architectures of the entire pipeline ...
This is partly due to the fact that state-of-the-art deep stereo matching networks, designed by humans, are already sheer in size. ...
It can be well extended to other dense matching tasks such as optical flow estimation and multi-view stereo. ...
arXiv:2010.13501v1
fatcat:xpag4ynauzd2bkyscve7fwqylu
DRI-MVSNet: A depth residual inference network for multi-view stereo images
2022
PLoS ONE
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It combines the channel attention mechanism and spatial pooling networks. ...
This study proposes a cascaded depth residual inference network, called DRI-MVSNet, that uses a cross-view similarity-based feature map fusion module for residual inference. ...
[25] proposed a framework for reconstructing the three-dimensional shape of an object from a pair of stereo images. ...
doi:10.1371/journal.pone.0264721
pmid:35320265
pmcid:PMC8942269
fatcat:nyrzqymhwvcqtmzxatwsbqtn7q
Towards Robotic Knee Arthroscopy: Multi-Scale Network for Tissue-Tool Segmentation
[article]
2021
arXiv
pre-print
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In this study, we present a densely connected shape aware multi-scale segmentation model which captures multi-scale features and integrates shape features to achieve tissue-tool segmentations. ...
As consequences, fully conventional network-based segmentation model suffers from long- and short- term dependency problems. ...
There are several advancements achieved to address this problem, for instance, non-local networks, attention mechanisms and multi-scale dense UNet [19] [20] [21] . ...
arXiv:2110.02657v1
fatcat:apd25s6r2zddnbop3esyq2vf3y
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
[article]
2021
arXiv
pre-print
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In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. ...
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. ...
We thank anonymous reviewers for their constructive comments. This work was funded in part by a research contract from Galen Robotics and in part by Johns Hopkins University internal funds. ...
arXiv:2011.02910v4
fatcat:q46pc3w2zzbnzcapmq5mpsrkaa
PA-MVSNet: Sparse-to-Dense Multi-View Stereo with Pyramid Attention
2021
IEEE Access
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The improved network is tested on the DTU dataset which is standard multi-view stereo benchmarks. ...
Considering the advantages of the point cloud in contrast to other 3D representations, a point-based multi-view stereo network (Point-MVSNet) is proposed by Chen et.al. ...
doi:10.1109/access.2021.3058522
fatcat:dsssicwkjrfrtkgwhtu3ppp2k4
2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43
2022
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction. ...
., +, TPAMI April 2021 1452-1459 High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction. ...
doi:10.1109/tpami.2021.3126216
fatcat:h6bdbf2tdngefjgj76cudpoyia
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.08566v1 fatcat:hnoaq6ov5zgd7kye6t5g2534iy
based on deep networks. ...
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. ...
Batsos and Mordohai applied a dense label correction algorithm, implemented as a recurrent, residual network, to an input disparity map estimated by a black box stereo algorithm. ...
arXiv:2004.08566v2
fatcat:wcwfgzibo5evbkun3atpsz6kwm
End-to-End Learning of Geometry and Context for Deep Stereo Regression
[article]
2017
arXiv
pre-print
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We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. ...
Luo et al. presented a notably faster network for computing local matching costs as a multi-label classification of disparities using a Siamese network [33] . ...
For each stereo image, we form a cost volume of dimensionality height×width×(max disparity + 1)×feature size. ...
arXiv:1703.04309v1
fatcat:c5tkljlnyba6dhoryxvikfqkue
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery
[article]
2018
arXiv
pre-print
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Stereo reconstruction techniques developed for terrestrial systems including self driving cars do not translate well to satellite imagery where image pairs vary considerably. ...
In this work we present neural network tailored for Digital Surface Model generation, a ground truthing and training scheme which maximizes available hardware, and we present a comparison to existing methods ...
Dense stereo matching with deep neural networks was also performed in Luo et al. [10] . ...
arXiv:1811.03535v2
fatcat:xdeihsbpcvfxla46iq4wm7folm
DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization
2022
Sensors
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dense depth maps from more low-dimensional codes. ...
Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. ...
feature matching; red points are sampled points for depth consistency residuals). ...
doi:10.3390/s22093389
fatcat:cx6f55lcjbhlfiazhsykppmaiu
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks
[article]
2019
arXiv
pre-print
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In this paper, a new deep learning architecture for stereo disparity estimation is proposed. ...
The proposed atrous multiscale network (AMNet) adopts an efficient feature extractor with depthwise-separable convolutions and an extended cost volume that deploys novel stereo matching costs on the deep ...
[14] proposed a Siamese network to match pairs of image patches for disparity estimation. ...
arXiv:1904.09099v1
fatcat:y6qzuxthy5ehhg5niz6d2a52d4
Authors List
2020
2020 National Conference on Communications (NCC)
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Residual Signal Using Linear
Sub Band Filters
Sidharth Aggarwal
P300 Based Stereo Localization of Single Frequency Audio Stimulus
Silpa Sanal Nair
Non-uniform Amplitude Codebooks for MU-MIMO in ...
Deep Neural Network for
Image Recognition
Aerial Multi-Object Tracking by Detection Using Deep Association
Networks
Blind Channel Coding Identification of RS Encoder Using Neural
Networks
Bharadwaj ...
doi:10.1109/ncc48643.2020.9056032
fatcat:tsdhbqblujfwlgf4ojr6ftqdhe
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
[article]
2018
arXiv
pre-print
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A key insight of this paper is that the network achieves a sub-pixel matching precision than is a magnitude higher than those of traditional stereo matching approaches. ...
This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved, quantization-free disparity ...
The output of this network is a 1-dimensional disparity residual that is then added to the previous prediction. We apply a ReLu to the sum to constrain disparities to be positive. ...
arXiv:1807.08865v1
fatcat:nbrbs5b2bbc7perju6nfib5toq
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