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Humans can perceive scenes in 3D from a handful of 2D views.
A morphable model for the synthesis of 3d faces
Volker Blanz and Thomas Vetter · 1999
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Statistical cues for domain specific image segmentation with performance analysis
Scott Konishi and Alan L Yuille · 2000
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Multiple View Geometry in Computer Vision
R. I. Hartley and A. Zisserman · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Cpmc: Automatic object segmentation using constrained parametric min-cuts
Joao Carreira and Cristian Sminchisescu · 2011
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Joao Carreira, Rui Caseiro, Jorge Batista, and Cristian Sminchisescu · 2012
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Indoor segmentation and support inference from RGBD images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Dense object reconstruction with semantic priors
Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, and Silvio Savarese · 2013
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Dense reconstruction using 3d object shape priors
Amaury Dame, Victor A. Prisacariu, Carl Y. Ren, and Ian Reid · 2013
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Multiscale combinatorial grouping
Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T Barron, Ferran Marques, and Jitendra Malik · 2014
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Class specific 3d object shape priors using surface normals
Christian Häne, Nikolay Savinov, and Marc Pollefeys · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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OpenDR: An approximate differentiable renderer
Matthew M Loper and Michael J Black · 2014
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Convolutional feature masking for joint object and stuff segmentation
Jifeng Dai, Kaiming He, and Jian Sun · 2015
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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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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Single-image depth perception in the wild
Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Feature space optimization for semantic video segmentation
Abhijit Kundu, Vibhav Vineet, and Vladlen Koltun · 2016
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Std2p: Rgbd semantic segmentation using spatio-temporal data-driven pooling
Yang He, Wei-Chen Chiu, Margret Keuper, and Mario Fritz · 2017
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Learning a multi-view stereo machine
Abhishek Kar, Christian Häne, and Jitendra Malik · 2017
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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
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Predicting deeper into the future of semantic segmentation
Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, and Yann LeCun · 2017
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Semanticfusion: Dense 3d semantic mapping with convolutional neural networks
John McCormac, Ankur Handa, Andrew Davison, and Stefan Leutenegger · 2017
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Self-supervised visual descriptor learning for dense correspondence
Tanner Schmidt, Richard Newcombe, and Dieter Fox · 2017
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Learning to synthesize a 4d rgbd light field from a single image
Pratul P Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, and Ren Ng · 2017
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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Deepvoxels: Learning persistent 3d feature embeddings
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer · 2019
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Pushing the boundaries of view extrapolation with multiplane images
Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely · 2019
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The replica dataset: A digital replica of indoor spaces
Julian Straub, Thomas Whelan, Lingni Ma, Yufan Chen, Erik Wijmans, Simon Green, Jakob J Engel, Raul Mur-Artal, Carl Ren, Shobhit Verma, et al · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G. Lowe · 2017
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Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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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
Cited alongside, same era.
3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
Abhijit Kundu, Yin Li, and James M. Rehg · 2018
Cited alongside, same era.
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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Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
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Articulation-aware canonical surface mapping
Nilesh Kulkarni, Abhinav Gupta, David Fouhey, and Shubham Tulsiani · 2020
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Consistent video depth estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image
Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujian Zheng, Jian Chang, and Jian Jun Zhang · 2020
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Associative3d: Volumetric reconstruction from sparse views
Shengyi Qian, Linyi Jin, and David F Fouhey · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Accelerating 3d deep learning with pytorch3d
Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari · 2020
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Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding
Mike Roberts and Nathan Paczan · 2020
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SynSin: End-to-end view synthesis from a single image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson · 2020
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Depth-supervised nerf: Fewer views and faster training for free
Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan · 2021
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Unsupervised learning of 3d object categories from videos in the wild
Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, and David Novotny · 2021
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Planar surface reconstruction from sparse views
Linyi Jin, Shengyi Qian, Andrew Owens, and David F Fouhey · 2021
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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 2021
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Shelf-supervised mesh prediction in the wild
Yufei Ye, Shubham Tulsiani, and Abhinav Gupta · 2021
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Learning to recover 3d scene shape from a single image
Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen · 2021
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pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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In-place scene labelling and understanding with implicit scene representation
Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew Davison · 2021
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