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Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases.
Determining optical flow
Berthold KP Horn and Brian G Schunck · 1981
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Stereo by intra-and inter-scanline search using dynamic programming
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A flexible new technique for camera calibration
Zhengyou Zhang · 2000
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Multiple View Geometry in Computer Vision
Richard I. Hartley and Andrew Zisserman · 2004
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Stereo processing by semiglobal matching and mutual information
Heiko Hirschmuller · 2007
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The unreasonable effectiveness of data
Alon Halevy, Peter Norvig, and Fernando Pereira · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A benchmark for the evaluation of rgb-d slam systems
Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers · 2012
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Sun3d: A database of big spaces reconstructed using sfm and object labels
Jianxiong Xiao, Andrew Owens, and Antonio Torralba · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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A deep visual correspondence embedding model for stereo matching costs
Zhuoyuan Chen, Xun Sun, Liang Wang, Yinan Yu, and Chang Huang · 2015
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Deep image homography estimation
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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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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Surfacenet: An end-to-end 3d neural network for multiview stereopsis
Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, and Lu Fang · 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Demon: Depth and motion network for learning monocular stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
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DeepMVS: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
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Learning for disparity estimation through feature constancy
Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang · 2018
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Neighbourhood consensus networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, and Josef Sivic · 2018
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Benchmarking 6dof outdoor visual localization in changing conditions
Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
Cascade cost volume for high-resolution multi-view stereo and stereo matching
Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan · 2020
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Epipolar transformers
Yihui He, Rui Yan, Katerina Fragkiadaki, and Shoou-I Yu · 2020
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Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
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Normal assisted stereo depth estimation
Uday Kusupati, Shuo Cheng, Rui Chen, and Hao Su · 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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Atlas: End-to-end 3d scene reconstruction from posed images
Zak Murez, Tarrence van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, and Andrew Rabinovich · 2020
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Learning to find good correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua · 2018
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DeepTAM: Deep tracking and mapping
Huizhong Zhou, Benjamin Ummenhofer, and Thomas Brox · 2018
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Cam-convs: Camera-aware multi-scale convolutions for single-view depth
Jose M Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, and Javier Civera · 2019
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Group-wise correlation stereo network
Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li · 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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Efficient neighbourhood consensus networks via submanifold sparse convolutions
Ignacio Rocco, Relja Arandjelović, and Josef Sivic · 2020
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SuperGlue: Learning feature matching with graph neural networks
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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AANet: Adaptive aggregation network for efficient stereo matching
Haofei Xu and Juyong Zhang · 2020
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Cost volume pyramid based depth inference for multi-view stereo
Jiayu Yang, Wei Mao, Jose M Alvarez, and Miaomiao Liu · 2020
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TransformerFusion: Monocular RGB scene reconstruction using transformers
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Perceiver IO: A general architecture for structured inputs & outputs
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Perceiver: General perception with iterative attention
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COTR: Correspondence transformer for matching across images
Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, and Kwang Moo Yi · 2021
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Automated detection of equine facial action units
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Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers
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Loftr: Detector-free local feature matching with transformers
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NeuralRecon: Real-time coherent 3D reconstruction from monocular video
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The bitter lesson
Richard Sutton · 2021
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A fast stereo matching network with multi-cross attention
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Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions
Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang · 2021
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Learning signed distance field for multi-view surface reconstruction
Jingyang Zhang, Yao Yao, and Long Quan · 2021
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