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Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space.
Optical models for direct volume rendering
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Image-based reconstruction of spatial appearance and geometric detail
Hendrik P. A. Lensch, Jan Kautz, Michael Goesele, Wolfgang Heidrich, and Hans-Peter Seidel · 2003
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A comparison and evaluation of multi-view stereo reconstruction algorithms
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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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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Joint 3d scene reconstruction and class segmentation
Christian Häne, Christopher Zach, Andrea Cohen, Roland Angst, and Marc Pollefeys · 2013
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Generative adversarial nets
Ian 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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Shapenet: An information-rich 3d model repository
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Structure-from-motion revisited
Johannes Lutz Schönberger and Jan-Michael Frahm · 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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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson and Christoph H. Lampert · 2020
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Leveraging 2D data to learn textured 3D mesh generation
Paul Henderson, Vagia Tsiminaki, and Christoph Lampert · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and Francois Fleuret · 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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Blockgan: Learning 3d object-aware scene representations from unlabelled images
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, and Niloy Mitra · 2020
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Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 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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Synsin: End-to-end view synthesis from a single image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su · 2021
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Unconstrained scene generation with locally conditioned radiance fields
Terrance Devries, Miguel Ángel Bautista, Nitish Srivastava, Graham W. Taylor, and Joshua M. Susskind · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 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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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Nerf-vae: A geometry aware 3d scene generative model
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, and Danilo Jimenez Rezende · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Lion: Latent point diffusion models for 3d shape generation
Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al · 2022
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Novel view synthesis with diffusion models
Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi · 2022
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Neural fields in visual computing and beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2022
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Point-nerf: Point-based neural radiance fields
Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann · 2022
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Generative multiplane images: Making a 2d gan 3d-aware
Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, and Alex Colburn · 2022
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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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Pixelsynth: Generating a 3d-consistent experience from a single image
Chris Rockwell, David F Fouhey, and Justin Johnson · 2021
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Geometry-free view synthesis: Transformers and no 3d priors, 2021
Robin Rombach, Patrick Esser, and Björn Ommer · 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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3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
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Renderdiffusion: Image diffusion for 3d reconstruction, inpainting and generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J Mitra, and Paul Guerrero · 2023
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Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Generative novel view synthesis with 3d-aware diffusion models, 2023
Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein · 2023
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Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction
Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su · 2023
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SDFusion: Multimodal 3d shape completion, reconstruction, and generation
Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tuyakov, Alex Schwing, and Liangyan Gui · 2023
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Learning to render novel views from wide-baseline stereo pairs
Yilun Du, Cameron Smith, Ayush Tewari, and Vincent Sitzmann · 2023
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Scenescape: Text-driven consistent scene generation, 2023
Rafail Fridman, Amit Abecasis, Yoni Kasten, and Tali Dekel · 2023
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Learning controllable 3d diffusion models from single-view images, 2023
Jiatao Gu, Qingzhe Gao, Shuangfei Zhai, Baoquan Chen, Lingjie Liu, and Josh Susskind · 2023
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3dgen: Triplane latent diffusion for textured mesh generation
Anchit Gupta, Wenhan Xiong, Yixin Nie, Ian Jones, and Barlas Oğuz · 2023
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Text2room: Extracting textured 3d meshes from 2d text-to-image models, 2023
Lukas Höllein, Ang Cao, Andrew Owens, Justin Johnson, and Matthias Nießner · 2023
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Noise2music: Text-conditioned music generation with diffusion models, 2023
Qingqing Huang, Daniel S. Park, Tao Wang, Timo I. Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Frank, Jesse Engel, Quoc V. Le, William Chan, Zhifeng Chen, and Wei Han · 2023
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Holodiffusion: Training a 3d diffusion model using 2d images, 2023
Animesh Karnewar, Andrea Vedaldi, David Novotny, and Niloy Mitra · 2023
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Neuralfield-ldm: Scene generation with hierarchical latent diffusion models, 2023
Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, and Sanja Fidler · 2023
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Neuralangelo: High-fidelity neural surface reconstruction
Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin · 2023
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Magic3d: High-resolution text-to-3d content creation
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin · 2023
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Realfusion: 360° reconstruction of any object from a single image
Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, and Andrea Vedaldi · 2023
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Mvdream: Multi-view diffusion for 3d generation, 2023
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang · 2023
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3d generation on imagenet, 2023
Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, and Sergey Tulyakov · 2023
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Viewset diffusion: (0-)image-conditioned 3d generative models from 2d data
Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi · 2023
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Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion
Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa · 2023
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Diffusion with forward models: Solving stochastic inverse problems without direct supervision
Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Joshua B. Tenenbaum, Frédo Durand, William T. Freeman, and Vincent Sitzmann · 2023
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Consistent view synthesis with pose-guided diffusion models, 2023
Hung-Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia-Bin Huang, and Johannes Kopf · 2023
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Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
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Multiview compressive coding for 3d reconstruction
Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari · 2023
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Diffusionerf: Regularizing neural radiance fields with denoising diffusion models
Jamie Wynn and Daniyar Turmukhambetov · 2023
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3d-aware image generation using 2d diffusion models, 2023
Jianfeng Xiang, Jiaolong Yang, Binbin Huang, and Xin Tong · 2023
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Dreamsparse: Escaping from plato’s cave with 2d frozen diffusion model given sparse views
Paul Yoo, Jiaxian Guo, Yutaka Matsuo, and Shixiang Shane Gu · 2023
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Sparsefusion: Distilling view-conditioned diffusion for 3d reconstruction
Zhizhuo Zhou and Shubham Tulsiani · 2023
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Sparse3d: Distilling multiview-consistent diffusion for object reconstruction from sparse views
Zi-Xin Zou, Weihao Cheng, Yan-Pei Cao, Shi-Sheng Huang, Ying Shan, and Song-Hai Zhang · 2023
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