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Multi-scale Context Attention Network for Stereo Matching

HaiWei Sang, QuanHong Wang, Yong Zhao
2019 IEEE Access  
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

Chenyang Duan, Nanfeng Xiao
2021 IET Computer Vision  
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]

Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Tom Drummond, Hongdong Li, Zongyuan Ge
2020 arXiv   pre-print
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

Ying Li, Wenyue Li, Zhijie Zhao, JiaHao Fan, Sen Xiang
2022 PLoS ONE  
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]

Shahnewaz Ali, Prof. Ross Crawford, Dr. Frederic Maire, Assoc. Prof. Ajay K. Pandey
2021 arXiv   pre-print
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]

Zhaoshuo Li, Xingtong Liu, Nathan Drenkow, Andy Ding, Francis X. Creighton, Russell H. Taylor, Mathias Unberath
2021 arXiv   pre-print
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

Ke Zhang, Mengyu Liu, Jinlai Zhang, Zhenbiao Dong
2021 IEEE Access  
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  
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]

Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, Stefano Mattoccia
2021 arXiv   pre-print
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]

Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, Adam Bry
2017 arXiv   pre-print
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]

Wayne Treible, Scott Sorensen, Andrew D. Gilliam, Chandra Kambhamettu, Joseph L. Mundy
2018 arXiv   pre-print
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

Mingle Zhao, Dingfu Zhou, Xibin Song, Xiuwan Chen, Liangjun Zhang
2022 Sensors  
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]

Xianzhi Du, Mostafa El-Khamy, Jungwon Lee
2019 arXiv   pre-print
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)  
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]

Sameh Khamis, Sean Fanello, Christoph Rhemann, Adarsh Kowdle, Julien Valentin, Shahram Izadi
2018 arXiv   pre-print
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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