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We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations.
Ray tracing volume densities
James T. Kajiya and Brian P Von Herzen · 1984
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Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman · 2004
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Generative Adversarial Nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
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ORB-SLAM: a versatile and accurate monocular SLAM system
Raul Mur-Artal, JMM M M Montiel, and Juan D Tardos · 2015
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Single-view to Multi-view: Reconstructing Unseen Views with a Convolutional Network
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2015
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Past, Present, and Future of Simultaneous Localization and Mapping: Towards the Robust-Perception Age
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose Neira, Ian D. Reid, and John J. Leonard · 2016
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A Large Dataset of Object Scans
Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, and Vladlen Koltun · 2016
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3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese · 2016
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Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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Structure-from-Motion Revisited
Johannes L. Schonberger and Jan-Michael Frahm · 2016
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Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee · 2016
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Hierarchical Surface Prediction for 3D Object Reconstruction
H Christian, Shubham Tulsiani, and Jitendra Malik · 2017
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A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Haoqiang Fan, Hao Su, and Leonidas Guibas · 2017
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A Survey of Structure from Motion
Onur Ozyesil, Vladislav Voroninski, Ronen Basri, and Amit Singer · 2017
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
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OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Semantic Scene Completion from a Single Depth Image
Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas Funkhouser · 2017
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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.
Interpretable transformations with encoder-decoder networks
Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, and Gabriel J. Brostow · 2017
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Neural Scene Representation and Rendering
S.M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu, and Demis Hassabis · 2018
Cited alongside, same era.
A Papier-Mache Approach to Learning 3D Surface Generation
Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry · 2018
Cited alongside, same era.
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
Eldar Insafutdinov and Alexey Dosovitskiy · 2018
Cited alongside, same era.
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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Dense 3D Object Reconstruction from a Single Depth View
Bo Yang, Stefano Rosa, Andrew Markham, Niki Trigoni, and Hongkai Wen · 2019
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SAL: Sign Agnostic Learning of Shapes from Raw Data
Matan Atzmon and Yaron Lipman · 2020
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State of the Art on Neural Rendering
A Tewari O Fried, J Thies V Sitzmann, S Lombardi K Sunkavalli, and R Martin-brualla T Simon J Saragih · 2020
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Differentiable Rendering: A Survey
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, and Adrien Gaidon · 2020
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Neural Sparse Voxel Fields
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Neural 3D Mesh Renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
Shubham Tulsiani, Saurabh Gupta, David Fouhey, Alexei A. Efros, and Jitendra Malik · 2018
Cited alongside, same era.
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
Cited alongside, same era.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, and Sanja Fidler · 2019
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Mesh R-CNN
Georgia Gkioxari, Jitendra Malik, and Justin Johnson · 2019
Cited alongside, same era.
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
Shichen Liu, Tianye Li, Weikai Chen, and Hao Li · 2019
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Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
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Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 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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PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, and Peter W. Battaglia · 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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MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur Bagautdinov, Pierre Baque, and Pascal Fua · 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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Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
Bo Yang, Sen Wang, Andrew Markham, and Niki Trigoni · 2020
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SALD: Sign Agnostic Learning with Derivatives
Matan Atzmon and Yaron Lipman · 2021
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Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll · 2021
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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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ShaRF: Shape-conditioned Radiance Fields from a Single View
Konstantinos Rematas, Ricardo Martin-Brualla, and Vittorio Ferrari · 2021
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IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser · 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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