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Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views.
Cooperative computation of stereo disparity
David Marr and Tomaso Poggio · 1976
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A volumetric method for building complex models from range images
Brian Curless and Marc Levoy · 1996
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Photorealistic scene reconstruction by voxel coloring
S.M. Seitz and C.R. Dyer · 1997
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Poxels: Probabilistic voxelized volume reconstruction
JS 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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A probabilistic framework for surface reconstruction from multiple images
Motilal Agrawal and Larry S Davis · 2001
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A probabilistic framework for space carving
A Broadhurst, T W Drummond, and R Cipolla · 2001
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Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman · 2003
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Poisson surface reconstruction
Michael M. Kazhdan, Matthew Bolitho, and Hugues Hoppe · 2006
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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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Real-time visibility-based fusion of depth maps
Paul Merrell, Amir Akbarzadeh, Liang Wang, Philippos Mordohai, Jan-Michael Frahm, Ruigang Yang, David Nistér, and Marc Pollefeys · 2007
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Patchmatch stereo - stereo matching with slanted support windows
Michael Bleyer, Christoph Rhemann, and Carsten Rother · 2011
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Screened poisson surface reconstruction
Michael M. Kazhdan and Hugues Hoppe · 2013
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Large scale multi-view stereopsis evaluation
Rasmus Ramsbøl Jensen, Anders Lindbjerg Dahl, George Vogiatzis, Engil Tola, and Henrik Aanæs · 2014
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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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Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 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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Efficient deep learning for stereo matching
W. Luo, A. Schwing, and R. Urtasun · 2016
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Pixelwise view selection for unstructured multi-view stereo
Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm · 2016
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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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Scenenet RGB-D: can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?
John McCormac, Ankur Handa, Stefan Leutenegger, and Andrew J. Davison · 2017
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OctNetFusion: Learning depth fusion from data
Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, and Andreas Geiger · 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.
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
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
Cited alongside, same era.
Deepmvs: Learning multi-view stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang · 2018
Cited alongside, same era.
Shape reconstruction using volume sweeping and learned photoconsistency
Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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DISN: Deep implicit surface network for high-quality single-view 3d reconstruction
Weiyue Wang, Xu Qiangeng, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 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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Nerd: Neural reflectance decomposition from image collections
Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch · 2020
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Implicit geometric regularization for learning shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
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Neural sparse voxel fields
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Vincent Leroy, Jean-Sébastien Franco, and Edmond Boyer · 2018
Cited alongside, same era.
Raynet: Learning volumetric 3d reconstruction with ray potentials
Despoina Paschalidou, Ali Osman Ulusoy, Carolin Schmitt, Luc van Gool, and Andreas Geiger · 2018
Cited alongside, same era.
Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
Cited alongside, same era.
Controlling neural level sets
Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, and Yaron Lipman · 2019
Cited alongside, same era.
Blenderproc
Maximilian Denninger, Martin Sundermeyer, Dominik Winkelbauer, Youssef Zidan, Dmitry Olefir, Mohamad Elbadrawy, Ahsan Lodhi, and Harinandan Katam · 2019
Cited alongside, same era.
Learning non-volumetric depth fusion using successive reprojections
Simon Donne and Andreas Geiger · 2019
Cited alongside, same era.
Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
Cited alongside, same era.
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Dist: Rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 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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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Learning implicit surface light fields
Michael Oechsle, Michael Niemeyer, Christian Reiser, Lars Mescheder, Thilo Strauss, and Andreas Geiger · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
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Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans
Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou · 2020
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D-nerf: Neural radiance fields for dynamic scenes
Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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Blendedmvs: A large-scale dataset for generalized multi-view stereo networks
Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan · 2020
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Multiview neural surface reconstruction by disentangling geometry and appearance
Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman · 2020
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Nerf++: Analyzing and improving neural radiance fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun · 2020
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Nerf in the wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
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Donerf: Towards real-time rendering of neural radiance fields using depth oracle networks
Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Chakravarty R. Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger · 2021
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Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
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NeRV: Neural reflectance and visibility fields for relighting and view synthesis
Pratul Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T. Barron · 2021
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