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We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images.
Poxels: Probabilistic voxelized volume reconstruction
Jeremy S De Bonet and Paul Viola · 1999
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A theory of shape by space carving
Kiriakos N Kutulakos and Steven M Seitz · 2000
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Handling occlusions in dense multi-view stereo
Sing Bing Kang, Richard Szeliski, and Jinxiang Chai · 2001
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Multi-camera scene reconstruction via graph cuts
Vladimir Kolmogorov and Ramin Zabih · 2002
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Variational stereovision and 3d scene flow estimation with statistical similarity measures
J-P Pons, Renaud Keriven, O Faugeras, and Gerardo Hermosillo · 2003
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Silhouette and stereo fusion for 3d object modeling
Carlos Hernández Esteban and Francis Schmitt · 2004
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A quasi-dense approach to surface reconstruction from uncalibrated images
Maxime Lhuillier and Long Quan · 2005
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski · 2006
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Using multiple hypotheses to improve depth-maps for multi-view stereo
Neill DF Campbell, George Vogiatzis, Carlos Hernández, and Roberto Cipolla · 2008
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Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2010
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Efficient large-scale multi-view stereo for ultra high-resolution image sets
Engin Tola, Christoph Strecha, and Pascal Fua · 2012
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Screened poisson surface reconstruction
Michael Kazhdan and Hugues Hoppe · 2013
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Massively parallel multiview stereopsis by surface normal diffusion
Silvano Galliani, Katrin Lasinger, and Konrad Schindler · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Towards probabilistic volumetric reconstruction using ray potentials
Ali Osman Ulusoy, Andreas Geiger, and Michael J Black · 2015
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Large-scale data for multiple-view stereopsis
Henrik Aanæs, Rasmus Ramsbøl Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl · 2016
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Deepstereo: Learning to predict new views from the world’s imagery
John Flynn, Ivan Neulander, James Philbin, and Noah Snavely · 2016
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Just look at the image: viewpoint-specific surface normal prediction for improved multi-view reconstruction
Silvano Galliani and Konrad Schindler · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes L Schönberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys · 2016
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 2017
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A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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Learned multi-patch similarity
Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc Van Gool, and Konrad Schindler · 2017
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Surfacenet: An end-to-end 3d neural network for multiview stereopsis
Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, and Lu Fang · 2017
Cited alongside, same era.
Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
Learning category-specific mesh reconstruction from image collections
Angjoo Kanazawa, Shubham Tulsiani, Alexei A Efros, and Jitendra Malik · 2018
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Neural 3d mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Learning efficient point cloud generation for dense 3d object reconstruction
Chen-Hsuan Lin, Chen Kong, and Simon Lucey · 2018
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Raynet: Learning volumetric 3d reconstruction with ray potentials
Despoina Paschalidou, Osman Ulusoy, Carolin Schmitt, Luc Van Gool, and Andreas Geiger · 2018
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Matryoshka networks: Predicting 3d geometry via nested shape layers
Stephan R Richter and Stefan Roth · 2018
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Cited alongside, same era.
End-to-end learning of geometry and context for deep stereo regression
Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry · 2017
Cited alongside, same era.
Tanks and temples: Benchmarking large-scale scene reconstruction
Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun · 2017
Cited alongside, same era.
From point clouds to mesh using regression
Lubor Ladicky, Olivier Saurer, SoHyeon Jeong, Fabio Maninchedda, and Marc Pollefeys · 2017
Cited alongside, same era.
Octnetfusion: Learning depth fusion from data
Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, and Andreas Geiger · 2017
Cited alongside, same era.
Surfnet: Generating 3d shape surfaces using deep residual networks
Ayan Sinha, Asim Unmesh, Qixing Huang, and Karthik Ramani · 2017
Cited alongside, same era.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
Shubham Tulsiani, Tinghui Zhou, Alexei A Efros, and Jitendra Malik · 2017
Cited alongside, same era.
Chengzhou Tang and Ping Tan · 2018
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Pixel2mesh: Generating 3d mesh models from single rgb images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
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Learning shape priors for single-view 3d completion and reconstruction
Jiajun Wu, Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T Freeman, and Joshua B Tenenbaum · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
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Learning to reconstruct shapes from unseen classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, and Jiajun Wu · 2018
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Deeptam: Deep tracking and mapping
Huizhong Zhou, Benjamin Ummenhofer, and Thomas Brox · 2018
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Point-based multi-view stereo network
Rui Chen, Songfang Han, Jing Xu, and Hao Su · 2019
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Learning single-image 3d reconstruction by generative modelling of shape, pose and shading
Paul Henderson and Vittorio Ferrari · 2019
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Dpsnet: End-to-end deep plane sweep stereo
Sunghoon Im, Hae-Gon Jeon, Stephen Lin, and In-So Kweon · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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3d scene reconstruction with multi-layer depth and epipolar transformers
Daeyun Shin, Zhile Ren, Erik B Sudderth, and Charless C Fowlkes · 2019
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Mvpnet: Multi-view point regression networks for 3d object reconstruction from a single image
Jinglu Wang, Bo Sun, and Yan Lu · 2019
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Deep view synthesis from sparse photometric images
Zexiang Xu, Sai Bi, Kalyan Sunkavalli, Sunil Hadap, Hao Su, and Ravi Ramamoorthi · 2019
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Recurrent mvsnet for high-resolution multi-view stereo depth inference
Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan · 2019
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